I'd even question that. The pre-LLM solutions were in most cases better. Searching a maintained database of curated and checked information is far better than LLM output (which is possibly bullshit).
Ditto to software engineering. In software, we have things call libraries: you write the code once, test it, then you trust it and can use it as many times as you want forever for free. Why use LLM generated code when you have a library? And if you're asking for anything complex, you're probably just getting a plagiarized and bastardized version of some library anyway.
The only thing where LLMs shine is a kind of simple, lazy "mash this up so I don't have to think about it" cases. And sometimes it might be better to just do it yourself and develop your own skills instead of use an LLM.
It's better to take an existing, already curated and tested library. Which, yes, may have been generated by an LLM, but has been curated beyond the skill of the LLM.
If you really can one shot it and it’s simple(left-pad). Great. But most things aren’t my that simple, the third time you have to think about it, it’s probably a net loss.
Eventually it gave up and commented out all the code it was trying to make work. Took me less than two minutes to figure out the solution using only my IDE's autocomplete.
It did save me time overall, but it's definitely not the panacea that people seem to think it is and it definitely has hiccups that will derail your productivity if you trust it too much.
"Tell me how to do X" (where X was, for one recent example, creating a Salt stanza to install and configure a service).
I do as it tells me, which seems reasonable on the face of it. But it generates an error.
"When creating X as you described, I get error: Z. Why?"
"You're absolutely correct and you should expect this error because X won't work this way. Do Y instead."
Gah... "I told you to do X, and then I'm going to act like it's not a surprise that X doesn't work and you should do something else."
If you ask for a link, it may hallucinate the link.
And unlike a search engine where someone had to previously think of, and then make some page with the fake content on it, it will happily make it up on the fly so you'll end up with a new/unique bit of fake documentation/url!
At that point, you would have been way better off just... using a search engine?
Which would certainly explain things like hallucinated references in legal docs and papers!
The reality is that for a human to make up that much bullshit requires a decent amount of work, so most humans don’t do it - or can’t do it as convincingly. LLMs can generate nigh infinite amounts of bullshit for cheap (and often more convincing sounding bullshit than a human can do on their own without a lot of work!), making them perfect for fooling people.
Unless someone is really good at double checking things, it’s a recipe for disaster. Even worse, doing the right amount of double checking makings them often even more exhausting than just doing the work yourself in the first place.
One time I tried to use Gemini to figure out 1950s construction techniques so I could understand how my house was built. It made a dubious sounding claim about the foundation, so I had it give me links and keywords so I could find some primary sources myself. I was unable to find anything to back up what it told me, and then it doubled down and told me that either I was googling wrong or that what it told me was a historical “hack” that wouldn’t have been documented.
These were both recent and with the latest models, so maybe they don’t fully fabricate links, but they do hallucinate the contents frequently.
Grok certainly will (at least as of a couple months ago). And they weren't just stale links either.
Now instead of the wikipedia article you are reading the exact same thing from google's home page and you don't click on anything.
It’s for queries that are unlikely to be satisfied in a single search. I don’t think it would be a negligible amount of time if you did it yourself.
On the other hand, where I think llms are going to excel, is you roll the dice, trust the output, and don't validate it. If it works out yayy you're ahead of everyone else that did bother to validate it.
I think this is how vibe coded apps are going to go. If the app blows up, shut down the company and start a new one.
I let Claude and ChatGPT type out code for me, while I focus on my research
wondering how is it going to work when they "search the web" to get the information, are they essentially going to take ad revenue away from the source website?
I think we all understand that at this point, so I question deeply why anyone acts like they don’t.
More convenient than traditional search? Maybe. Quicker than traditional search? Maybe not.
Asking random questions is exactly where you run into time-wasting hallucinations since the models don't seem to be very good at deciding when to use a search tool and when just to rely on their training data.
For example, just now I was asking Gemini how to fix a bunch of Ubuntu/Xfce annoyances after a major upgrade, and it was a very mixed bag. One example: the default date and time display is in an unreadably small "date stacked over time" format (using a few pixel high font so this fits into the menu bar), and Gemini's advice was to enable the "Display date and time on single line" option ... but there is no such option (it just hallucinated it), and it also hallucinated a bunch of other suggestions until I finally figured out what you need to do is to configure it to display "Time only" rather than "Data and Time", then change the "Time" format to display both data and time! Just to experiment, I then told Gemini about this fix and amusingly the response was basically "Good to know - this'll be useful for anyone reading this later"!
More examples, from yesterday (these are not rare exceptions):
1) I asked Gemini (generally considered one of the smartest models - better than ChatGPT, and rapidly taking away market share from it - 20% shift in last month or so) to look at the GitHub codebase for an Anthropic optimization challenge, to summarize and discuss etc, and it appeared to have looked at the codebase until I got more into the weeds and was questioning it where it got certain details from (what file), and it became apparent it had some (search based?) knowledge of the problem, but seemingly hadn't actually looked at it (wasn't able to?).
2) I was asking Gemini about chemically fingerprinting (via impurities, isotopes) roman silver coins to the mines that produced the silver, and it confidently (as always) comes up with a bunch of academic references that it claimed made the connection, but none or references (which did at least exist) actually contained what it claimed (just partial information), and when I pointed this out it just kept throwing out different references.
So, it's convenient to be able to chat with your "search engine" to drill down and clarify, etc, but a big time waste if a lot of it is hallucination.
Search vs Chat has anyways really become a difference without a difference since Google now gives you the "AI Overview" (a diving off point into "AI Mode"), or you can just click on "AI Mode" in the first place - which is Gemini.
Everyone is entitled to their own opinion, but I asked ChatGPT and Claude your XFCE question, and they both gave better answers than Gemini did (imo). Why would you blindly believe what someone else tells you over what you observe with your own eyes?
If you're using ChatGPT like you use Google then I agree with you. But IMO comparing ChatGPT to Google means you haven't had the "aha" moment yet.
As a concrete example, a lot of my work these days involves asking ChatGPT to produce me an obscure micro-app to process my custom data. Which it usually does and renders in one shot. This app could not exist before I asked for it. The productivity gains over coding this myself are immense. And the experience is nothing like using Google.
It might seem quaint today but one example might be fact checking a piece of text.
Google effectively has a pretty good internal representation of whether any particular document concords with other documents on the internet, on account of massive crawling and indexing over decades. But LLMs let you run the same process nearly instantly on your own data, and that's the difference.
We already know many useful things to do; there are already 10,000 startups (9789 out of YC alone, 4423 of which are coding-related) doing various ostensibly useful things. And there a ton more use-cases discussed in the comments here and elsewhere. But because of the headline the discussion is missing the much more important point!
Satya's point is, we need to do things that improve people's lives. Specific quotes from TFA:
>... "do something useful that changes the outcomes of people and communities and countries and industries."
> "We will quickly lose even the social permission to take something like energy, which is a scarce resource, and use it to generate these tokens, if these tokens are not improving health outcomes, education outcomes, public sector efficiency, private sector competitiveness, across all sectors, small and large, right?" said Nadella. "And that, to me, is ultimately the goal."
Which is absolutely right. He's the only Big Tech CEO I've heard of who constantly harps on the human and economic benefit angle of LLMs, whereas so many others talk -- maybe in indirect ways -- about replacing people and/or only improving company outcomes (which are usually better for only a small group of people: the shareholders.)
He's still a CEO, so I have no illusions that he's any different from the rest of them (he's presided over a ton of layoffs, after all.) But he seems to be the only CEO whose interests appear to be aligned with the rest of ours.
There has to be gold in the West! Look at all of the prospectors moving there to get rich on gold! You have not demonstrated that there are 10k uses of AI, you've only demonstrated that there are 10k "businesses" interested in making money off of AI. Just like there were 10k+ crypto-currencies... Just like there were 10k+ "uber but for..." apps. Where are these failed gold-rush attempts now?
Investors are currently rewarding the words "AI", so (to extend the analogy) when the gold moved, the gold rushers moved to where they thought the gold would be.
Also your emphasis doesn't change the reading of the sentence.
"Uber but for ..." apps were either just bad ideas, or ended up serving niche markets or reincarnating as features in Uber, DoorDash and the like. The only innovation there was new facets of the gig economy, which is still expanding BTW.
Now if you look at the AI gold rush, the differences are stark:
1. A lot of AI startups are already making a lot of money and growing at a record pace. Some of the numbers out there are bonkers.
2. The domains they are targeting are all over, including accounting, education, energy, games, healthcare, sales, pharma, drug discovery, agriculture, legal, customer support, semiconductor design, travel, retail... you name it.
3. The demand is so high, AI hyperscalers have TRIPLE-DIGIT BILLIONS EACH in backlog, i.e. commited revenue they could not realize because of severe capacity crunch.
4. Literally all the world's major governments, which typically take ages to catch up to technological change, are scrambling to get in on the AI wave.
All within only ~3 years.
I'd say there's some utility there.
Do you have examples of this? I'm aware of raises, but not aware of any profitable ai companies yet
https://a16z.com/revenue-benchmarks-ai-apps/
https://www.cnbc.com/2025/03/15/y-combinator-startups-are-fa...
https://medium.com/@gjarrosson/ycs-revenue-explosion-497ea17...
https://stripe.com/blog/inside-the-growth-of-the-top-ai-comp...
https://www.ft.com/content/a9a192e3-bfbc-461e-a4f3-112e63d0b...
https://menlovc.com/perspective/2025-the-state-of-generative...
The revenue growth -- assuming these investors are not all colluding to fudge these numbers on a grand scale -- is way higher than what most have seen before.
Not everyone is hitting PMF of course, but apparently the success rate is also way higher than the past. Ignore the valuations and funding numbers, they are definitely inflated due to the hype.
In any other industry, this is the death knell for your company. It's only in tech where the investors drop so much money on early investments, that companies can get away with losing money for "2-3 years".
>The revenue growth -- assuming these investors are not all colluding to fudge these numbers on a grand scale -- is way higher than what most have seen before.
As a matter of strict numbers? The revenue growth could be higher just because of inflation.
You really seem to be doing a Gish Gallup here of links, which don't really prove any of your points. If there are specific metrics you can point to in these links that prove your point, then please call that out, but every one of these that I have clicked on has felt like a nothing burger.
> Menlovc link: is just talking about investment in AI, which proves my point
> FT: Paywall
> Stripe: You've got correlation, but not causation. An alternative thing you're proving could be that more investment in the space makes it easier to bring a product to market and earn revenue (How does this compare to SaaS Startups 5-10 years ago)
> Medium Link: is more of "proving my original point"
> CNBC: It's not clear what the metric is here that's making your case
> a16z: It looks to me like, this is actually proving the point that every one of these is considering "investments" as "revenue" which is wild.
That is all.
I think that there are some leaders who think about building a business in terms of providing value or an exchange of goods.
I think there’s a fundamental difference to society between a ceo that abstracts their role as building a product people value, and one that sees their role as hacking the stock price. The first still may do terrible things, but I think a lot of modern problems in society stem from the fact that deregulation changed the incentives from (1) to (2), but I’m pro any conversation that tries to move us back towards 1
Is your contention that we don't already know how to do useful things with AI?
I don't think you meant "ostensibly".
However, i would say Satya Nadella is quite different from other American CEOs (whose mantra is "greed is good") in that he comes from an Indian middle class family whose focus was on education/good-work and also being forced to take care of a son who suffered from cerebral palsy. All of these shaped his worldviews to be more empathetic of the "common man" which is reflected in his style of leadership.
"forced"?
That said, he's competing in circles dominated by absolute sociopaths. I can't imagine how you could battle with monsters in the abyss and not get Nietzsche'd at least a little bit.
As this article itself alludes, in-spite of sinking a large amount of money into OpenAI he is genuinely looking for ways to make it useful rather than just make money.
A way to drum up sense of urgency without mentioning that it's the patience of the investors (and _not_ the public) that will be the limiting factor here?
It's not even a solution in search of a problem, it's a tool in search of a reason to use it as a solution to a problem on such a scale that it justifies the billions of dollars of money we've poured into it while driving up the cost of fresh water, electricity, RAM, storage, data centre space, and so on.
Consumer adoption also happened organically over time, catalyzed mostly by email and instant messaging, which were huge technological leaps over fax and snail mail. IBM and DEC didn't have to jam "Internet" buttons all over their operating systems to juice usage (although AOL certainly contributed to filling landfills with their free trial disks).
Feels like this combination (usually) creates a race to the bottom instead of expansion of new ideas.
LLMs kind of feel somewhere in the middle
This isn't one of those times.
I’m spending $400/mo on AI subscriptions at this point. Probably the best money I spend.
but lots of folks were broke as hell and miserable
I'd say for an estate that I am the executor of, it probably saved me $50k in legal fees and other expenses because it helped me analyze a novel problem and organize it ask the right questions of counsel.
Another scenario i had to deal with i needed a mobile app to do something very specific for a few weeks. I specced out a very narrowly useful iphone application, built it out on the train from DC to NYC, and had it working to my satisfaction the next day. Is it production code ready for primetime? Absolutely not. But I got capability to do what I needed super quickly that my skill level is no longer up to the task to accomplish!
IMO, these things let you make power tools, but your ability to get value is capped by your ability to ask the right questions. In the enterprise, they are going to kill lots of stupid legacy software that doesn't add alot of value, but adds alot of cost.
The technology is amazingly powerful. Full stop.
The constraint that drives cost is technical — semiconductor prices. Semiconductors are manufactured commodities over time, those costs will drop over time. The Sun workstation I bought for $40k in 1999 would get smoked by a raspberry pi for $40.
Even if everyone put their pencils down and stopped working on this stuff, you’d get a lot of value from the open source(-ish) models available today.
Worst case scenario, LLMs are like Excel. Little computer programs will be available to anyone to do what they need done. Excel alone changed the world in many ways.
that $400 will go up by at least a factor of 10 once the bubble pops
would you be prepared to pay $4000/month?
I doubt that the exponential cost explosion day is coming. When the bubble pops, the bankruptcies of many of the players will push the costs down. US policy has provided a powerful incentive for Chinese players to do what Google has done and have a lower cost delivery model anyway.
> the bankruptcies of many of the players will push the costs down
the running costs don't disappear because people go broke
The cost iceberg with this stuff isn’t electricity, it’s the capital.
Other than Google and Facebook, the big hype players can’t produce the growth required to support the valuations. That’s why the OpenAI people started fishing for .gov backstops.
The play is get the government to pay and switch out whatever Nvidia stuff they have now with something more efficient in a few years.
If a country/state has to choice of giving power to data center A or B, it makes sense for Satya to make statements about how only Microsoft provides the most AI value
I guess you could always just use a fraction of the billions in investments and whip up a few new power plants. [1]
What the hell is going on in this type of argument anyways? Utilities are normally private businesses so what does the state have to do with it?
He's blaming customers that his product isn't hitting the valuation he wants.
If they mean "machine learning", then sure there are application in cancer detection and the like, but development there has been moving at a steady pace for decades and has nothing to do with the current hype wave of GenAI, so there's no reason to assume it's suddenly going to go exponential. I used to work in that field and I'm confident it's not going to change overnight: progress there is slow not because of the models, but because data is sparse and noisy, labels are even sparser and noisier, deployment procedures are rigid and legal compliance is a nightmare.
If they mean "generative AI", then how is that supposed to work exactly? Asking LLMs for medical diagnosis is no better than asking "the Internet at large". They only return the most statistically likely output given their training corpus (that corpus being the Internet as a whole), so it's more likely your diagnosis will be based on a random Reddit comment that the LLMs has ingested somewhere, than an actual medical paper.
The only plausible applications I can think of are tasks such as summarizing papers, acting as augmented search engines for datasets and papers, or maybe automating some menial administrative tasks. Useful, for sure, but not revolutionary.
This from a huge LLM skeptic in general. It doesn't have to be right all the time if it in aggregate saves time doctors can spend diagnosing you.
At best and if you're lucky to have a receptive doctor you can use it to nudge them in the right direction. But until direct to consumer sales for medical equipment and tests are allowed, the medical profession is well insulated. It is impossible by regulation to "take healthcare into your own hands" even if you want to.
It's a more-or-less intentional equivocation between different meanings of AI, as you note, machine learning vs generative AI. They want to point at the real but unsexy potential of ML for medical use in order to pump up the perceived value of LLMs. They want to imply to the general public and investors that LLMs are going to cure cancer.
Obviously still double check things, but it was moment of clarity I hadn't really had before this. Still needed the doctor and all the experience to diagnose and fix things, but relaying that info back to me is something doctors are only okay at. Try it out! take a summary sheet of a recent visit or incident and feed it in.
For instance, as a SWE, I get just a little help with boilerplate from the AI. I could usually have done it better, but sometimes the ask is both simple enough and boring enough that the code from the LLM actually produces something very close to what I would produce.
On the other side of the coin, a non-technical person using AI would be unable to properly understand and review the output.
Where it shines is on things that I am OK at. Like writing marketing copy. I can get by myself, but its slightly outside of my wheelhouse, but as long as I have a solid understanding of the product I can use AI to compliment my beginner/intermediate skills and produce something better than I would produce on my own.
A similar thing is writing tutorials. I write some code and documentation, but the tutorials are enough of a slog that I get distracted by my distaste for it. This is a good fit for AI.
I think this is where we will see AI help the most. Where someone's skillset includes the task at hand but at a secondary level where the user might doubt themselves or get distracted with the misery the task brings them.
If the proverbial marketer that you were referring to had some experience with coding, I dont see why they wouldnt be able to review the output and see any obvious flaws.
My whole point is that LLMs are of limited use when you are already an expert or when you know nothing about the subject. However, they really seem to help elevate beginner/intermediate level tangential skillsets.
Obviously everything is still evolving and your results may vary.
WT actual F? They invested so much into something what is not obvious brings value? Will there be consequences on them? Or they take the bonus and hide in New Zealand bunker?
It's big money betting on narratives from wanna-be-big money how AI is transformative for the future. Public takes all the risks with hardware and energy inflation or bailing out banks out of investments which require pruductivity growth from AI which we don't yet see in statistics.
We took the wrong turn somewhere. And responsible people don't seem to be capable or willing to change the course. Too much power in too few weak minds. Nothing good will come from this.
That’s courageous from a CEO of an US company, where the current government doesn’t see burning more oil as being bad for the planet, and is willing to punish everyone who thinks otherwise.
Cause they are able to search the web deeply, search for up to date info/research and synergize all that. You can have back and fourth for as long as you need.
The issue is that using LLMs properly requires a certain skill that more people lack.
And I don't mean I've just rejected the lowest offer to DIY shit to oblivion. I've accepted bids and been continuously disappointed with the results. To the point I no longer trust "experts" in these spaces because any "expertise" they bring is pretty shallow at best. The exception I'd point out is the auto-shop I prefer. They are busy enough that if I want to schedule something it's going to be 3-6 months out. As a result I've replaced my own suspension and replaced my alternator and starter myself while waiting for appointments with the rare actual experts in any domain. Actual expertise is rare and most folks don't know how to recognize it, especially outside of domains they are familiar with. Unfortunately it's way more profitable to fake expertise in various domains and collect payments and run than to actually stand behind your business and work. Thus the world we're forced to live in today.
This probably has nothing to do with gen AI (the kind of AI Nadela is speaking about).
Though it's a use case people like Satya will want to avoid for reasons.
LLMs will be used for aggressive, yet incredibly subtle manipulation, consensus building, and response tracking.
20-40% of social media is already bots, and in the future it is likely you will not be able to reply to anything anywhere without a bot either 1) responding, or 2) logging and sending your response to multiple parties instantly.
If the Stasi had LLMs the Berlin Wall would have never fallen
* Higher electricty bills.
* 5-6x cost of RAM, GPUs, and other computer components
* Data centers popping up in their backyards
* An internet inundated with slop
* Slop beginning to infiltrate the video game industry and other creative industries
* AI being used to justify gutting entry level jobs for a generation already screwed by larger, long horizon economic forces
* Grok enabling the creation of revenge porn and CSAM with seemingly no repercussions
* Massive IP theft on a scale previously unheard of
* Etc.
The pros of AI are:
* It can summarize text and transcribe audio decently well.
* It can make funny pictures of cats wearing top hats.
* ???
And no, I'm not saying the technology is bad. The business isn't going swimmingly, though.
There are plenty of uses for AI. Right now, the industry is heavily spending on training new models, improving performance of existing software and hardware, and trying to create niche products.
Power usage for inference will drop dramatically over the next decade, and more models are going to run on-device rather than in the cloud. AI is only going to become more ubiquitous, there's 0% chance it 'fails' and we return to 2020.
Only because companies have been cutting costs for decades here. This is not a good argument for AI.
> writing software
If you mean typing characters quickly, yes. Otherwise, there’s still a lot of employed devs, with many AI companies hiring.
> writing docs about software
The most useful docs are there because they contain info you cannot determine from the code. AI is not able to do this.
> computer graphics (animation, images)
If you are producing slop, yes.
> driving cars
True, but only because of its improved physical awareness. ie it’s a mechanical gain (better eyes, ears, etc) not an intellectual one (interpreting that information). Self driving cars aren’t LLMs and not really applicable here. Entirely different field.
> AI is only going to become more ubiquitous, there's 0% chance it 'fails' and we return to 2020
Absolutely true. But not for the reasons you think.
An AI might be better than an indian call center but I doubt that when the AI is made by indians anyway.
> writing software, writing docs about software
I have asked AI about exactly one topic and it lied about the API of a library making up the functions I was supposed to call.
> computer graphics (animation, images)
I have indeed seen many wonderful meme images come out of the generators but that was before they got lobotomized for producing that subject matter
[EDIT]
And the worst part is these are all just more "software as a service" designed to remove the possibility of using a tool without approval.
There obviously are some compelling use cases for "AI", but it's certainly questionable if any of those are really making people's lives any better, especially if you take "AI" to mean LLMs and fake videos, not more bespoke uses like AlphaFold which is not only beneficial, but also not a resource hog.
I think there are business reasons why they wouldn’t do that, and that makes me sad.
Every time it hallucinates visits to Starbucks.
I never go to Starbucks, it’s just a probable finding given the words in the question.
This should work. I want it to work. But until it can do this correctly all analysis capabilities should be suspect.
Even a year ago I had success with Claude giving it a photo of my credit card bill and asking it to give me repeating category subtotals, and it flawlessly OCR'd it and wrote a Python program to do as asked, giving me the output.
I'd imagine if you asked it to do a comparison to something else it'd also write code to do it, so get it right (and certainly would if you explicity asked).
When non techie friends/family bring up AI there are two major topics: 1) the amount of slop is off the charts and 2) said slop is getting harder to recognize which is scary. Sometimes they mention a bit of help in daily tasks at work, but nothing major.
They don't find AI useful, just a toy. Is their fault? Maybe.
> They don't find AI useful, just a toy. Is their fault? Maybe.
Hi there, friends from another dimension! In my reality, there's a cold front coming from the north. Healthcare is expensive and politics are a mess. But AI? It hallucinates sometimes but it's so much better for searching, ad hoc consultation and as a code assistant than anything I've ever seen. It's not perfect, but it saved me SO much time I decided to pay for it. I'm a penny pincher, so I wouldn't be paying for it otherwise.
I think Satya is talking about cost/benefit. AI is incredibly useful but also incredibly expensive. I think we still need to find the right balance (perhaps slower model releases), but there's no way we'll put the genie back in the bottle.
I hope your AI gets better! Talk to you later!
I have access to all the popular AI tools from work for free, I use them for the same cases you mentioned like search, consultation, a better StackOverflow, and autocomplete. It’s definitely useful but I would describe that as incrementally useful, not revolutionary.
Satya is saying that AI needs to start doing more than vibe coding and autocomplete, there’s probably half a trillion invested into the technology worldwide now and it’s not enough for AI to be a good coding assistant. It needs to replace customer support, radiologists, and many other professions to justify the unprecedented level of investment its garnered.
AI is subsidized for the users
No brainwr.
[0]: https://www.bloomberg.com/graphics/2025-ai-data-centers-elec...
So you can easily add 1-200 dollars to your bill for one day of higher usage.
https://www.myhorrynews.com/news/horry-electric-co-op-to-cha...
Copilot Notepad.
Copilot MS Paint.
Copilot Shoes.
Copilot Ice Cream.
Copilot Liposuction Surgery Machines.
LOL. "Looks like you're trying to tie those laces - would you like me to order you velcro?"
With all this useless slop, he’s literally arguing against his own point.
Might not work well with those relying on structured finance to break computer component markets, but does well for the majority that are buying their equipment with regular cash.
And yet studies show the opposite [0].
[0] https://www.media.mit.edu/publications/your-brain-on-chatgpt...
His bottom line depends on this bet that everyone is going to depend on AI and pay Microslop rent to use it.
https://scholar.google.ca/scholar?q=cognitive+effects+of+ai+...
I’ve been predicting for a while: free or cheap AI will enshittify and become an addictive ad medium with nerfed capabilities. If you want actually good AI you will have to pay for it, either a much heftier fee or buying or renting compute to run your own. In other words you’ll be paying what it actually costs, so this is really just the disappearance of the bubble subsidy.
So this is something that factors in hugely in planning permission. What do we get back for it is a question asked a lot. And datacenters are notoriously bad at providing jobs, during construction yes but in the run phase it's mainly low-value remote hands and security stuff.
But they aren't stupid. You sound like a tech bro.
That's the problem.