Microsoft cancels leases for AI data centers, analyst says

Yeah the guy who designed the Courier for Microsoft was smarter and better informed than me, the problem is he was smarter and better informed than his leadership at microsoft who canned the project in favour of doing nothing, messing around with nokia, and ending up with a watered down product years too late in terms of the surface.

You mean Tim “Apple intelligence” Cook

Ha ha

I’ve used windows and macOS side by side for over a decade, and macOS BY FAR has a greater level of polish than windows, which to this day is relatively unstable. I haven’t had myself or a relative experience an OS-level crash in macOS in like five years years. Meanwhile, troubleshooting a family member’s BSOD is a regular occurrence.

Not saying Apple is great but for me MS UX has been an utter crap, both in managed corp env and on a personal device.

Remember Apple Maps? They polish stuff incrementally.

> almost as good as Slack

I'm sorry, what?! I will grant you almost as good as Zoom but Teams' actual chat functionality is awful.

I can't fathom why they didn't just clone Slack for their chat UX, it would have been less effort and better.

To be fair, they did somehow manage to take over javascript while nobody was looking.

.NET is a compelling product, cross platform, open source, well supported, good tooling.

Not convinced.

In 2018:

https://www.gov.uk/government/news/health-and-social-care-se...

> Matt Hancock has banned the NHS from buying fax machines and has ordered a complete phase-out by April 2020.

The NHS is quite federated. Hell many parts of it are independent companies. Some trusts have decent modern systems though - I had to go for a test just before christmas - I'd phoned up my GP in the morning got an appointment for half an hour later, he ordered a test, said go to one of these 8 centres, so I went to one about half an hour away (I live a fair way away from a major town). Had the test, by the time I'd had lunch and driven back home I had another call from the GP asking me to come in that evening, the appointment was created by the GP and read seconds later at the hospital, the test was done there and results reported back again at the click of a system at the GP.

But that's just my local trust. Go 10 miles west and it's another trust and they have different systems. And I had to go to one of the test centres in the trust, I couldn't go to one in a neighbouring trust as they have different systems and there's no/limited interconnects.

The healthcare system I largely use in the US was bought up by another larger system. They are now primarily on an electronic system (which for me is welcome) but some of the offices still use faxes as well.

Hah, that isn’t a brownfield solution.

These orgs could hire someone who could solve these issues right now (and for the last decade) if they would allow those people to exist in their org.

The challenge is precisely that those people (or people with that capability) aren’t allowed to exist.

Completely unusable in any bank, or almost any organization dealing with data secrecy. You have complex, often mandatory processes to onboard folks. Sure, these can be improved but hiring some 'Bob' would be far from smooth sailing.

Big enough corps will eventually have their own trusted 'Bobs' just like they have their own massive cluster farms (no, AWS et al is not a panacea and its far from cheap&good solution).

Giving any form of access to some remote code into internal network of a company? Opsec guys would never ack that, there is and always will be malice coming from potentially all angles.

Honestly, I think the mode that will actually occur is that incumbent businesses never successfully adopt AI, but are just outcompeted by their AI-native competitors.

> I think you don’t have widespread adoption until AI takes the form of a plug-and-play “remote employee”.

Exactly. The problem with the AGI-changes-everything argument is that it indirectly requires "plug-and-play" quality AGI to happen before / at the same time as specialized AGI. (Otherwise, specialized AGI will be adopted first)

I can't think of a single instance in which a polished, fully-integrated-with-everything version of a new technology has existed before a capable but specialized one.

E.g. computers, cell phones, etc.

And if anyone points at the "G" and says "That means we aren't talking about specialized in any way," then you start seeing the technological unlikeliness of the dominoes falling as they'd need to for AGI fast ramp.

There hardly are any plug and play human employees.

Given how bad some of the apps and websites are I am not sure phone calls are any worse! They are also less prone to data breaches and the like.

“ only takes phone calls for appointments” is a huge selling point for a physicians office. People are very tired of apps.

Today I spent 55 minutes in a physical store trying to get a replacement for two Hue pendant lights that stopped working. The lights had been handed in a month ago and diagnosed as "can't be repaired" two weeks ago. All my waiting time today was spent watching different employees punching a ridiculous amount of keys on their keyboards, and having to get manual approval from a supervisor (in person) three times. I am now successfully able to wait 2-6 weeks for the replacement lights to arrive, maybe.

When people say AI is going to put people out of work, I always laugh. The people I interacted with today could have been put out of work by a well-formulated shell script 20 years ago.

This. Thank you.

People stop caring about optimising or improving stuff. Even programmers are guilty of it. I haven't changed my vimrc in over 5 years

IMO it comes from inertia. People at the top are not digital-native. And they're definitely not AI-native.

So you're retrofitting a solution onto a legacy org. No one will have the will to go far enough fast enough. And if they didn't have the resources to engineer all these software migrations who will help them lead all these AI migrations?

Are they going to go hands off the wheels? Who is going to debug the inevitable fires in the black box that has now replaced all the humans?

It's not a resource problem; everyone inside the system has no real incentive to do anything innovative; improving something incrementally is more likely to be seen as extra work by your colleagues and be detrimental to the person who implemented it.

What's more likely is a significantly better system is introduced somewhere, NHS can't keep up and is rebuilt by an external. (Or more likely it becomes a inferior system of a lesser nation as the UK continues its decline).

I dont know that getting rid of those wacky Excel sheets is a prerequisite to having AI work. We already have people like Automation Anywhere watching people hand carve their TPS reports so that they can be recreated mechanistically. Its a short step from feeding the steps to a task runner to feeding them to the AI agent.

Paradigm shifts in the technology do not generally occur simultaneously with changes in how we organize the work to be done. It takes a few years before the new paradigm backs into the workflow and changes it. Lift and shift was the path for several years before cloud native became a thing, for example. People used iPhone to call taxi companies, etc.

It would be a shame to not take the opportunity to tear down some old processes, but, equally, sometimes Chesterton's fence is there for good reason.

To be honest, that kind of sounds like a dystopian hell: chatGPT writing memos because we can't be arsed, and the reading the same memos because neither can the recipient. Why even bother with it?

Since I don't have an expendable army, I must be either an idiot or a troglodyte. Where my understanding falters is finding a domain where accuracy and truth aren't relevant. In your example you said nothing about a "phased rollout", is that even germane to this scenario? Is there appreciable "financial risk?" Are you personally qualified to make that judgement? You put your name at the bottom of this letter and provided absolutely no evidence backing the claim, so you'd best be ready for the shitstorm. I don't think HR will smile kindly on the "uh idk chatgpt did it" excuse.

Random non-technical people dealing with CSVs is probably one of the best usecases for current AI tools too.

Technical people don't write code, they (along with product people) specify things exhaustively. While in theory a super AGI can do everything (therefore deprecating all of humanity) in reality I suspect that given existing patterns in orgs of layers of managers who don't like to wade into the details specifying to the nth degree there will be a need for lots of SMEs except that AI will probably be a leaky abstraction and you'll still need technical people to guide the automation efforts

Given that you invented the term in your paraphrase, what do you mean by "fast takeoff AGI"?

Highly regarded people unite :D

More seriously though: unless you have privileged information or have done truly extensive research, do not short stocks. And if you do have privileged information, still don't short stocks because unless you have enough money to defend yourself against insider trading like Musk and it's ilk, it's not going to be worth it.

It's perfectly reasonable to determine that a particular high growth stock is not going to perform as well going forward, in which case I'd shift allocation to other, better candidates.

Generally, being long equities is a long term positive expected value trade. You don't have to time the market, just be persistent. On the other hand, as you correctly alluded to, shorting equities requires decently precise timing, both on entry and exit.

A common hypothesis for why Nvidia is so hot is because they have an effective monopoly on the hardware to train AI models and it requires a crap ton of hardware.

With DeepSeek it’s been demonstrated you can get pretty damn for a lot cheaper. I can only imagine that there are tons of investors thinking that it’s better to invest their dollars in undercutting the costs of new models vs investing billions in hardware.

The question is, can Nvidia maintain their grip on the market in the face of these pressures. If you think they can’t, then a short position doesn’t seem like that big of a gamble.

The combination of high and climbing price to earnings ratios for a smaller subset of tech firms, outsize retail investment in tech (cloaked by people buying crypto), and macro environment factors like high interest rates stimulating risky lending has me swapping this bubble toward the top of the list.

See further: https://www.morningstar.com/news/marketwatch/20250123167/six...

Bubble sort is very resource-hungry...

> If you want to grow the real economy, build houses and reduce the cost of living.

Yes, I wonder why it is so hard for Western countries to understand that there's no future in a place where housing is more expensive than your average salary. If may look cool for a few years until most people have left or are living on the streets.

A friend tried to tell me China has a real estate crisis, because the value of houses is dropping due to building to many and people are losing on their investments. I asked him if he is sure cheap and available housing is a crisis.

Every time I contact an enterprise for support, the person I'm talking to gets lots of things wrong too. It takes skepticism on my part and some back and forth to clean up the mess.

On balance AI gets more things wrong than the best humans and fewer things wrong than average humans.

> let's assume robotics become to a extremely high level, everything humen work can be reduced to 1/100 with help of robots, what will happen next?

We work 35 hour years instead of 35 hour weeks?

You’re describing exactly what happened during both the Industrial Revolution and the advent of computer automation.

Prior to computerization and databases, millions of people were required for filing, typing, and physically transporting messages and information. All of those jobs, entire fields of work were deleted by computerization.

Just in the past couple months there have been a number of "I am a senior/principal engineer and this is how I use LLMs". I would agree that the tools are not optimal yet but every iteration has improved for me.

Maybe whatever language you are coding it or whatever project you are working on is not a good fit? It is an equally perplexing situation for myself when I hear anecdotes like yours which don't align with my experience. The fact that you say everything is garbage calls into question either how you are using the tool or something else.

I can reliably use cursor's composer to reference a couple files, give a bullet list of what we are trying to do and point it to one of the better models and the output is junior engineer level or better output. When I say junior, I mean a junior who has experience with the codebase.

I have a similar experience. Tried to use it for real work and got frustrated by the chat’s inability to say “I don’t know”. It’s okay for code snippets demonstrating how something can be used (stack overflow essentially), also code reviews can be helpful if doing something for the first time. But they fail to answer questions I’m interested in like “what’s the purpose of X”.

> All I hear is anecdotal statements from people claiming LLMs have made them some percent more productive. Yet few actually say how or demonstrate it.

It's very difficult to measure productivity of most people, certainly most people in office jobs, so while you can have a gut feeling that you're doing better, it's no more measurable than pre-AI individual productivity measurement was

If LLM chatbots are making you vastly more productive in a field, you are in the bottom 20% of that field.

They're still useful tools for exploring new disciplines, but if you're say a programmer and you think ChatGPT or DeepSeek is good at programming, that's a good sign you need to start improving.

I think its about scope and expectations. I have had some form of AI code completer in my neovim config for 3 years. It works flawlessly and saves me tons of keystrokes. Sure sometimes it suggests the incorrect completion but I just ignore it and keep coding as if it didn't exist. I am talking about line by line, not entire code blocks, but even that it does well at times.

From what I have seen the people that have the most success have AI building something from scratch using well known tooling (read: old tooling).

The problem is that doesn't immediately help most people. We are all stuck in crap jobs with massive, crusty code bases. Its hard for AI because its hard for everyone.

I fixed the hinge in my oven by giving perplexity.com the make and problem. I saved an hour on the phone calling people to organise a visit some time in the next week.

Maybe you should stop using the Ai slop tools that don't work?

I would like to propose a moratorium on these sorts of “AI coding is good” or “AI coding sucks” comments without any further context.

This comment is like saying, “This diet didn’t work for me” without providing any details about your health circumstances. What’s your weight? Age? Level of activity?

In this context: What language are you working in? What frameworks are you using? What’s the nature of your project? How legacy is your codebase? How big is the codebase?

If we all outline these factors plus our experiences with these tools, then perhaps we can collectively learn about the circumstances when they work or don’t work. And then maybe we can make them better for the circumstances where they’re currently weak.

I agree with the other commenter that said if you're "vastly" more productive as a developer due to AI, you probably weren't that good to begin with. Otherwise, please provide concrete examples.

Myself, I do find it quite useful in a few respects. First and foremost, as a "better Google/StackOverflow." If something's not working, I can describe my exact scenario and usually get pointed in the right direction. Sometimes the LLM just wastes my time by very confidently telling me some function/library that solves my exact problem exists when in fact it doesn't.

Second, IntelliJ's local LLM is sort of a smarter autocomplete. It makes some outright wrong suggestions, but when there's areas where I have to do a lot of repetitive tasks that follow a simple pattern (like for instance, mapping fields from one type of object to another), it does a pretty good job of making correct suggestions. I definitely appreciate it but it's certainly not doing things like writing a significant portion of code in my style.

I've been using Amazon Q Developer as it was provided and approved by my employer. It has been pretty good with Python codebases, Kubernetes configurations, and (not surprisingly) CDK/Cloudformation templates. I can pretty much just ask it "here's my python script, make everything I need to run it as a lambda, hook that lambda up to x, it should run in a vpc defined in this template over here", and it'll get all that stuff put together and its normally pretty solid code it generates. It seems to pull in a lot of the context of the project I have open. For instance, I can say "it should get those values from the outputs in other-cf-template.yml" and it knows the naming schemes and what not across templates, even if it didn't generate those templates.

I might go back and tweak some stuff, add some extra tags and what not, but often its pretty good at doing what I ask.

Sometimes its suggestions aren't what I was really wanting to do in my codebase, a handful of times it has made up methods or parameters of even well-known libraries. But usually, its suggestions are better than a basic IntelliSense-style autocomplete at least in my experiences.

I haven't used many of the other developer assistant plugins like say GitHub Copilot. I couldn't really say which is better or worse. But I do think using Q Developer has made me faster in many tasks.

I wouldn't expect a tool that doesn't have access to the context of my editor and the files I have open to be very useful for actually coding. There's a lot of context to understand in even a basic application. If you're just asking a locally running app in ollama "give me a method to do x", don't be surprised if it doesn't know everything else happening in your app. Maybe it'll give you a halfway decent example of doing something, but devoid of how it actually plugs in to whatever you're making it might be entirely worthless.

Seriously. It’s like half of the people in this thread are living in a completely different world.

And this is coming from someone who uses LLMs daily at the subscription, API (vscode and 3 nextjs apps) and local level. I have a custom langchain stack, prompt repo, you name it. And regardless of how little or how much I use what I have, or what soup de jour prompt or process (from Keep it simple to Prompt enhancers) I can’t say it’s made a meaningful difference in my life. Even with all of the customization and planning.

It’s a great search engine though.

Or fix a leaking faucet.

LLM direct response models are quite mature, yes (4o)

LLM based MoE architectures with some kind of reasoning process ( Claude 3+, o series, R1, grok 3 with thinking ), are the equivalent of v0.2 atm, and they're showing a lot of promise.

It's pretty good for a whole class of problems that humans currently do.

Think of it like you're talking to someone so smart that they answer before you're finished explaining, and get the general idea wrong, or seem really pedantic and your misplaced use of a past tense verb that should have been active tense causes then to completely reinterpret what you're talking about. Think of our current LLMs like idiot savants, and trust them as much.

I don't use AI to write code if that code is not short and self contained. It's great at explaining code, great at strategy and design about code. Not so much at actually implementing code larger than 1/4 to 1/3rd it's output context window. After all, it's not "writing code", it's statistically generating tokens that look like code it's seen before. It's unknown if the training code in which the LLM is statistically generating a reply actually ran, it could have been pseudo code explaining that computer science concept, we don't know.

People seem to want a genie that does what they are thinking, and that is never going to work (at least with this technology.) I'm really talking about effective communications, and understanding how to communicate with a literal unreal non-human construct, a personality theater enhanced literary embodiment of knowledge. It's subtle, it requires effort on the user's side, more than it would if one were talking to a human expert in the area of knowledge you operate. You have to explain the situation so the AI can understand what you need, and developers are surprising bad at that. People in general are even worse at explaining. Implied knowledge is rampant in developer conversation, and an LLM struggles with ambiguity, such as implied references. Too many same acronyms in different parts of tech and science. It does work, but one really needs to treat LLMs like idiot savants.

In my case its coding real world apps that people use and pay money for. I no longer personally type most of my code, Instead I describe stuff or write pseudo code that LLMs end up converting into the real thing.

It's very good at handling the BS part of coding but also its very good at knowing things that I don't know. I recently used it to hack a small bluetooth printer which requires its own iOS app to print, using DeepSeek and ChatGPT I was able to reverse engineer the printer communication and then create an app that will print whatever I want from my macOS laptop.

Before AI I would have to study how Bluetooth works now I don't have to. Instead, I use my general knowledge of protocols and communications and describe it to the machine and I'm asking for ideas. Then I try things and ask the stuff that I noticed but I don't understand, then I figure out how this particular device works and then describe it to the machine and ask it to generate me code that will do the thing that I discovered. LLMs are amazing at filling the gaps in a patchy knowledge, like my knowledge of Bluetooth. Because I don't know much about Bluetooth, I ended up creating a CRUD for Bluetooth because that's what I needed when trying to communicate and control my bluetooth devices(it's also what I'm used to from Web tech). I'm bit embarrassed about it but I think I will release it commercially anyway.

If I have a good LLM under my hand, I don't have to know specialised knowledge on frameworks or tools. General understanding of how things works and building up from there is all I need.

It's like a CNC machine but for coding.

Either you’re using it wrong, or you are using the wrong tools.

For search, try kagi fastgpt (no subscription required):

https://kagi.com/fastgpt

For code completion, I’ve found it’s not good at jumping hard hurdles, but it is a bit better than find replace (e.g. it finds things that are syntactically different, but semantically related), and can notice stuff like “you forgot to fix the Nth implementation of the interface you just extended”.

It’s also good at “I need to do something simple in a language I do not know”.

I’ve definitely encountered ai slop from coworkers. I’m sure they also produce stack overflow copy paste garbage too. Dealing with their newly-found increased “productivity” is an open problem.

Insisting on strict static typing helps. The LLMs can’t help with that, and it forces a higher bar before compilation succeeds.

> What is using AI seriously?

It's buying into the hype. AI is crypto 2.0, the true believers live in their own bubble and their own fantasy world.

I use it to get the keywords and ideas, then use the normal search engine to get the facts. Still, even in this limited capacity I find the LLMs very useful.

This is exactly the experience I've had. Recently started learning OpenTofu(/Terraform) and the company now had Gemini as part of the Workspace subscription. It was great to get some basic going, but very quickly starts suggesting wrong or old or bad practices. Still using it as a starting point and to help known what to start searching for, but like you said, it's only slightly better than a regular search engine.

It's a figurative speech, obviously its a spectrum where some believe that AGI is around the corner or that all this is nothing more than some overblown statistics exercise and LLMs have nothing to do with actual intelligence.

In my opinion, this generation of AI is amazing but isn't it.

Of the three, QC is different in that it's not a solution looking for a problem. If we ever scale QC to the point where it can do meaningful work (the "if" is doing a lot of work there - per your point about hardware), then I don't see it fumbling like the other two have. We have immediate and pressing needs that we know how to solve with QC. The other two are very much research coming up with cool toys, and product fucking around so that they can find out what to use them for.

There's a wikipedia page addressing your exact concern: https://en.wikipedia.org/wiki/Lump_of_labour_fallacy

https://en.wikipedia.org/wiki/Slavery_in_Egypt https://en.wikipedia.org/wiki/Slavery_in_ancient_Egypt

You're missing the key phrase. "Says it will". Companies, of course, say all sorts of things. Sometimes, those things come to pass. But not really all that often.

The 500B is just something to put on a press release.

It cost Apple $0 to say they'll spend $500B.

The "LLM Siri" hasn't been rolled out even in beta, estimates reckon 2026

https://www.macrumors.com/2024/11/21/apple-llm-siri-2026

Just like all the other voice assistants.

I’m sorry but what are you saying.

How are any of these unique competitive advantages over iCloud, App Store, Safari, and just generally more locked-in high margin mobile platform users than anyone?

> Apple will not beat Microsoft in any capacity here

I'm sure MS will provide AI to business, but if Apple get things right, they'll be the biggest provider of AI to the masses.

With a Siri that knows your email, calendar, location, history, search history, ability to get data from and do things in 3rd party Apps (with App Intents) and if it runs on your phone for security, it could be used by billions of consumers, not a few hundred million MS office users.

What was that restaurant I went to with Joan last fall? Send linkedin requests to all the people I've had emails from company X.

Of course they could take too long or screw things up.

I don't think Microsoft legally has access to any Teams enterprise data like chats and recordings.

That's nothing new, politicians lie all the time.

Everyone's response when a politician announcing some supposed massive spending program should be to say "show me the appropriation bill and receipts, then we can talk."

Microsoft has committed $100B to Stargate. These cuts are meaningless by comparison.

so, historically speaking, who should i short? ;)

When you double down on the incorrect reporting instead of retracting or correcting it, as Bloomberg did with their ludicrous spy chip story, it becomes FUD regardless of your initial intent.

"Years later, Bloomberg doubles down on disputed Supermicro supply chain hack story" - https://www.datacenterdynamics.com/en/news/years-later-bloom...