- MSFT CapEx is up 45% YoY, positioning for inference dominance

- MSFT is prioritizing AI inference for millions of customers over massive AI training for OpenAI

- Losing inference demand risks clients moving to AWS/GCP

- MSFT taking bet that industry overbuild will lead to more attractive leasing terms in future vs owning

- OpenAI chases AGI; MSFT chases scalable, affordable AI that drives Azure adoption

- Leadership is risk adjusted - they win if OpenAI succeeds, and win if open-source wins

There is almost certainly at least one person in Redmond working on this that’s smarter and better informed than you, just going off probability.

this isn't the actual test though right? it's surely true, and yet companies make bad decisions all the time. It needs to be conditioned on "someone smarter and better informed than you _with influence in the organization sufficient to enact change_"

And it isn't even that affordable.

You are in their ecosystem, you will use their products.

It took them 12 years to make Teams almost as good as Slack.

Azure only has made it this far because its Microsoft, so many of their roll outs have been terrible (looking at you AKS, Cosmos DB).

Copilot is used at my wifes company because they get it for free, and it seriously seems like its ChatGPT 3.5 still

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.

- Existing data-centre footprints might have enough planned hardware swaps.

- There might be better places to lease data centers for power. The US, Canada, and others all have perspectives on this.

M$ Nadella is great at business strategic decisions. If only there is another great product person at M$ that pushes the design of their software and hardware. Both has gotten a lot better but still not Apple level of polish.

Absurd claim given the untenable OS release cadence under Tim Cook.

"But better than M$" is still undeserving of the lofty implication from "level of polish".

- I can't tell if my macbook is charging or not while the lid is closed. There is no light indicator - minimize/maximize buttons are very small given that I am using a the touchpad. I am not saying they are unsuably small but far from ideal. - If I fully deplete the battery. I cannot immediatly turn it on with adapter's power. Why? Every windows laptop can be run with adapter even with a dead battery.

> BSOD is a regular occurrence

I hate windows as much as the next guy, but are you really getting BSOD in 2025?

I like linux and I know its not exactly polished but it offers something as a trade off which neither MacOS nor Windows do.

First you should not be depleting modern batteries, its not good for the battery health, or indeed storage health, recall that solid-state storage integrity is not guaranteed in the absence of electrical current (a reminder to those who have a habit of backing up stuff onto SSDs, unplugging them and forgetting about them for extended time periods).

Second, if you think about it, its a safety feature in to protect you from data corruption.

If they let you turn on a machine with a fully-depleted battery and you immediately yank out the power cord, then you risk data corruption.

Sure, modern solid-state storage (at least the high quality implementations) have power-loss-protection. But that relies on capacitors. And to charge up capacitors, you need what ... oh yeah, that's right, electricity .... ;-)

So that is most likely why Apple require you to have a minimal charge before allowing you to power-on from completely depleted.

And to be honest, what are you bitching about anyway. Most Apple devices I use only require a 5–10 minute charge from completely depleted to get you to the minimum required. So just go make a cup of coffee and remind yourself not to completely deplete batteries in the future.

I would remind those sorts of people of the 5 P's ...

Prior Planning Prevents Poor Performance

Starting a zoom or presentation on low battery is just a fucking stupid thing to do. There's no other way to put it.

And if you really must have an ugly and un-necessary light indicator, Apple have an answer for that....

Do not charge off USB-C, instead buy an Apple magsafe charger. That comes with a little indicator light on the end of the cord.

Alternatively, if you are charging off USB-C all you have to do on a powered mac is to look at the battery indicator in the menu bar.

* He sees data center leases getting cheaper in near future due to everyone building

* He’s investing big in AI, but in every year there needs to be a rough balance between capacity and need for capacity

* He doesn’t necessarily believe in fast takeoff AGI (just yet)

* Even if there is fast takeoff AGI, he thinks human messiness will slow implementation

* It won’t be a winner-take-all market and there will be plenty of time to scale up capacity and still be a major player

Five years ago during covid all these clunky legacy businesses couldn't figure out a CSV let alone APIs but suddenly with AGI they are going to become tech masters and know how to automate and processize their entire operations?

I found it very amusing that at the turn of the decade "digitalisation" was a buzzword as Amazon was approaching its 25th anniversary.

Meanwhile huge orgs like the NHS run on fax and was crippled by excel row limits. Software made a very slow dent in these old important slow moving orgs. AI might speed up the transition but I don't see it happening overnight. Maybe 5 years if we pretend smartphone adoption is indicative of AGI and humanoid robot rollout

You click a button on Microsoft Teams and hire “Bob” who joins your team org, gets an account like any other employee, interacts over email, chat, video calls, joins meetings, can use all your software in whatever state it’s currently in.

It has to be a brownfield solution because most of the world is brownfield.

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.

Solarwinds.

Local agent machines for cloud CI/CD pipelines.

Devs using npm and pypi and such.

To be a bit reductive, `apt-get update` or equivalent.

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.

their CEO was a believer of making his departments compete for resources leading to a brutal, dysfunctional clusterfuck. rent seeking behavior on the inside as well as outside.

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.

There's an argument here something like.. if you can replace each role with an AI, you can replace multiple with a single AI, why not replace the structure with a single person?

And the answer is typically that someone has deemed it significant and necessary that decision-making in this scenario be distributed.

Better hope that coder doesn’t decide to just take all the money and run to a non extradition jurisdiction though, or the credentials to that DB get leaked.

Just look at names. Firstname, lastname? Jejeje, no.

Treating them as constants? laughs in married woman.

If you can absolutely, 100% cast iron guarantee that one identity field exactly identifies one living person (never more, never less), these problems are trivial.

If not? Then its complexity might be beyond the grasp of the average DOGE agent (who, coincidentally, are males in their early 20s with names conforming to a basic Anglo schema).

And that's just the NAME field.

Most of those orgs have people in key positions (or are structurally setup in such a way) that isn’t desirable to change these things.

As long as we have complex manual processes that even the people following them can barely remember the reason why they exist, we will never be able to get AI to smooth it over. It is horrendously difficult for real people to figure out what to put in a TPS report. The systems that you refer to need to be engineered out or organisations first. You don't need AI for that, but getting rid of millions of excel files is needed before AI can work.

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.

If suddenly the NHS had a team of "free" genuinely phd-level AGI engineers working 24/7 they'd make a bunch of progress on the low-hanging fruit and modernize and fix a whole bunch of stuff pretty rapidly I expect.

Of course the devil here is the requirements and integrations (human and otherwise). AGI engineers might be able to churn out fantastic code (some day at least), but we still need to work out the requirements and someone still needs to make decisions on how things are done. Decision making is often the worst/slowest thing in large orgs (especially public sector).

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).

AGI, by most definitions at least, would be better than most people at most things. Especially if you take OpenAI's definition, which boils it down only to economic value, a company would seemingly always be better off replacing everything with AGI.

Maybe more likely. AGI would just create superior businesses from scratch and put human companies out if business.

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 just we programmers who want to automate everything.

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.

The hilarious thing is there is absolutely no improvement AI could possibly make in the experience you've described. It would just be a slower, costlier, more error prone version of what can easily be solved with a SQL database and a web app.

Apps (actually a PWA) mean I can choose an appointment at any time in the day and know that I have a full palette of options over the next few days. The same App(PWA) allows me to click through to my NHS login where I can see my booked appointments or test results.

We should submit our code to a mainframe and everyone is going to improve their skills too.

The whole point of an AGI is that you don't need to be a tech master (or even someone of basic competence) to use it, it can just figure it all out.

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.

With a well working rag system you can find the reason why any decision was made so long as it was documented at some point somewhere. The old share point drives with a billion unstructured word documents starting from the 1990s is now an asset.

Copy some of the results. Be surprised that you didn't think of some of them.

"Rewrite the email succinctly and in a more casual tone. Mention [results from previous prompt + your own thoughts]. Reference these pricing pages: [URL 1], [URL 2]. The recipient does not appreciate obvious brown nosing."

This is where the HI comes into the equation.

Our current intellectual milieu is largely incapable of nuance—everything is black or white, on or off, good or evil. Too many people believe that the AI question is as bipolar as every other topic is today: Will AI be godlike or will it be worthless? Will it be a force for absolute good or a force for absolute evil?

It's nice to see someone in the inner circles of the hype finally acknowledge that AI, like just about everything else, will almost certainly not exist at the extremes. Neither God nor Satan, neither omnipotent nor worthless: useful but not humanity's final apotheosis.

I agree completely though, it's nice to see a glimmer of sanity.

EDIT: My best hope is this bubble bursts catastrophically, and puts the dotcom crash to shame. Then we might get some sensible regulation over the absolute deluge of fraudulent, George Jetson spacecamp bull shit these clowndick companies spew on a daily basis.

If by "messiness" he means "the general public having massive problems with a technology that makes the human experience both philosophically meaningless and economically worthless", then yeah, I could absolutely see that slowing implementation.

I'm a hobby musician. There are better musicians. I don't stop.

I like to cook. There are better cooks. I don't stop.

If you are Einstein or Michael Jordan or some other best-of-the-best, enjoyment of life and the finding of worth/meaning can be tied to absolute mastery. For the rest of us, tending to our own gardens is a much better path. Finding "meaning" at work is a tall order and it always has been.

As for "economically worthless," yes if you want to get PAID for writing code, that's a different story, and I worry about how that will play out in the scope of individual lifetimes. Long term I think we'll figure it out.

Bit of a click bait title, but it certainly seems like the realization is setting in that the hype has exceeded near term realistic expectations and some are walking back claims (for whatever reason; honesty, derisking against investor securities suits, poor capital allocation, etc).

Nadella appears to be the adult in the room, which is somewhat refreshing considering the broad over exuberance.

1) Wouldn't want to go back

2) Wouldn't believe that it's about to replace human intellectual work

In other words AI got advanced enough to do amazing things but not 500B or T level of amazing and people with the money are not convinced that it will be anytime soon.

RLHF trains it to fool humans into thinking it’s authoritative, not to actually be correct.

But, I am also having more fun building things than perhaps the earliest days of my first code written, which is just over 7 years now. Insofar as 1) goes, yes, I never want to go back. I can learn faster and more deeply than I ever could. It's really exciting!

I’ve tried to use it as a web search replacement and often the information is generic or tells me what I already know or wrong.

I’ve used a code suggestions variant long before OpenAI hype started and while sometimes useful, rarely is it correct or helpful on getting over the next hurdle.

Any code from my coworkers is now just AI slop they glanced over once. Then I spend a long time reviewing and fixing their code.

I really don’t find spending time writing long form questions so a bot can pretend to be human all that time saving, especially if I have to clarify or reword it to a specific “prompt-engineer” quality sentence. I can usually find the results faster typing in a few keywords and glancing at a list of articles. My built in human speed reading can determine if what I need is probably in an article.

LLM seriousness has made my job more difficult. I would prefer if people did go back.

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.

LLMs so far seem to be good at developing prototype apps. But most of my projects already have codegen and scaffolding tools so I guess I don’t get that use out of them.

I predict that once you release your embarrassing app, you will find all the corner cases and domain secrets come rearing out with little ability of the LLM to help you (especially with Bluetooth).

For other stuff, I still find it very useful because why would I bother to code something non-novel when I can just tell the LLM what I need?

For example, if I need a code that finds the device that given characteristics belongs(bluetooth stuff, again) to I can just tell the LLM to write it for me. It doesn't take a genius to write such a code, its elemental stuff and I would rather not spend my working memory on remembering the structures and names of variables. I copy+paste the current class that handles the bluetooth comms, tell it that I need a function for sending data to the printer and it gives me back the result. There's no art in writing such a code, its standard code for an API and I would prefer not to bother with it.

And

“It's very good at handling the BS part of coding…”

This is the part that I think is difficult in a team situation.

Learning and understanding is the important part, and certainly isn’t BS.

I understand that it really can make it seem like velocity has increased when you really are shipping things that more or less “work”, but it’s really a good practice to understand your code.

I’ve had to spend a significant amount of time fixing work that was admittedly generated using AI by other engineers, and I really fear engineers are beginning to trade deep understanding for the high of getting something that “works” with little effort.

It might “work” but you might be ignoring the work everyone around you is doing to clean up your brittle code that doesn’t scale and wasn’t thought through at inception.

For example just today, I dumped a crash log from the Mac version of Microsoft Remote Desktop into it. This damn app locks up 10 times a day for me causing a "Force Quit" event and subsequent crash dump to be generated. Normally what can I do with that crash dump other than send it off to Apple/Microsoft? It identified where it thought the crash was coming from: excessive right clicking causing some sort of foundational error in their logic. Avoiding right clicking has solved the issue for me. Now that I write this out, I could have spent hours upon hours finding a needle in a haystack and that would probably made me a better developer but the bar is too high, there is too much other work I have to get done than to chase this. Instead I would have just lived with it. Now I have some closure at least.

Again it seems like everyone has got their own unique stories. Is AI taking everything over? Not yet. Can I go back to pre-AI? No, its like going back to Windows 95.

Code quality actually doesn't matter when you remove the human from the loop as long as it works correctly because it becomes something made by a machine to be interpreted by a machine.

As for security, that mostly stems from the architecture. LLMs mediocracy also helps with following industry conventions and best practices.

In my case I never get the code being written at once, instead I make LLMs write pieces that I put together myself. Never got used to copilot or Cursor, I feel in control only with the chat interface.

Bugs happen but its not that different from any other type of bugs. Also, you end up learning about Bluetooth as bugs and other unexpected behavior happen. The great thing about LLMs is that they are interactive, so for example when collecting Bluetooth packets for analysis I ended up learning that the communication with Bluetooth is a bit like talking through a middleman and some packet types are only about giving instructions to the Bluetooth chip and others are actually about communicating with a connected device.

Using LLM for coding something you don't understand is much different than Googling something, then copy+paste a snippet from Stackoverflow because you can ask for instant explanation and modifications for testing edge cases and other ideas.

I got into coding about a decade ago when cheap outsourcing had been all the rage for a number of years. A lot of my early experience was taking over very poorly written apps that had started off with fast development and then quickly slowed down as all of the sloppy shortcuts built up and eventually ground development to a halt. There's a decent chance LLMs lead to another boom in that kind of work.

For products that won't be scaled, I imagine it becomes just another abstraction layer, with the cost of human input outweighing the cost of the additional infrastructure / beefing up hardware to support the inefficiencies created.

Yea idk about that one chief. I have been working in ML (specifically scaling of large model training) at FAANG for the past 8 years, and have been using AI for my work since basically the first time this became even slightly usable, and I don’t share your optimism (or pessimism depending on how you see it). Yes it’s still pretty bad, but you have to look at rate of improvement, not just a static picture of where we are today.

I might still be wrong though and you may be right, but claiming that anyone using AI believes like you do is flat out false. A lot of my colleagues also working in ML researcher think like me btw.

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

"Us self-claiming some [artificial general intelligence] milestone, that's just nonsensical benchmark hacking to me. So, the first thing that we all have to do is, when we say this is like the Industrial Revolution, let's have that Industrial Revolution type of growth. The real benchmark is: the world growing at 10 percent. Suddenly productivity goes up and the economy is growing at a faster rate. When that happens, we'll be fine as an industry."

That's a completely different statement from "AI is generating no value"!

(I always thought that this 'evil microsoft' trope is a sign of stupidity, now i am having second thoughts...)

(Not entirely sure what the next fad will be, but some sort of quantum computing thing doesn't feel unlikely. Lot of noise in that direction lately.)

That seems like a low bar because it already is- it's just not equally distributed yet.

My own productivity has grown far more than 10% thanks to AI, and I don't just mean in terms of dev. It reads my bloodwork results, speeds up my ability to repair a leak in my toilet tank, writes a concise "no I won't lend you money; I barely know you" message... you name it.

Normally all of those things would take much longer and I'd get worse results on my own.

If that's what I can do at the personal level, then surely 10% is an easily-achievable improvement at the enterprise level.

For the last year, I've tried all sorts of models both as hosted services and running locally with llama.cpp or ollama. I've used both the continue.dev vscode extension and cursor more recently.

The results have been frustrating at best. The user interface of the tools is just awful. The output of any models from Deepseek to quen to Claude to whatever other model is mediocre to useless. I literally highlight some code that includes comments about what I need and I even include long explicit descriptions etc in the prompts and it's just unrelated garbage out every time.

The most useful thing has just been ChatGPT when there's something I need to learn about. Rubber ducking basically. It's alright at very simple coding questions or asking about obscure database questions I might have, but beyond that it's useless. Gotta keep the context window short, or it starts going off the rails every single time.

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 do believe the benefit decreases the more senior or familiar the work is but there is still a noticeable benefit and I think it largely depends on the velocity and life cycle of the product. I think you get less benefit the slower the velocity or the more mature of a product. To deny it like in your post is simply being an intellectual minimalist.

You make a polite but still ad hominem "attack" about me instead of addressing my points with demonstrations of evidence.

Make a video or blog article actually showing how your use of LLMs in coding is making you more productive. Show what it's doing to help you that has a multiplier effect on your productivity.

I don't think it deserves a video or blog, like I already said the multiple posts that have made HN front page have covered it well. - Autocomplete saves me keystrokes usually - Features like Cursor's composer/agent allow me to outsource junior level changes to the code base. I can copy/paste my requirements and it gives me the diffs of the changes when its done. Its often at a junior level or better and tackles multi-file changes. I usually kick this off and go make other changes to the code base.

Now like I have said before, this depends a lot on the velocity of the team and the maturity of the code base. I think more mature products you will have less benefit on feature implementation and most likely more opportunity in the test writing capabilities. Likewise, teams with a slower cadence, thinking a bluechip software company compared to a startup, are not going to get as much benefit either.

Instead of being so aggressive, simply say why it does not work for you. These tools strive in web dev which you may not be involved in!

they don't let me walk at the pace of a SUV.

AI is like the good shoes. they help, and make many tasks a bit easier. but they can't make me into an SUV.

and if they can, then no programmers will have jobs. which is the end-state of this whole LLM thing as far as I can tell.

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.

Whereas measuring productivity and usefulness is way more opaque.

Many simple software systems are highly productive for their companies.

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 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.

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.

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.

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.

I swear half the time when I use it to look up the nuances of system API stuff, it's replaying forum, mailing list or Stackoverflow conversations that Google ought to be able to find but somehow can't.

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

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

We have been living in a fake economy for quite some time where money is printed and distributed to the "tech" sector. Which isn't really "tech", but mostly entertainment (YouTube, Netflix, Facebook, ...).

Growth of the economy means nothing. The money that has been printed goes to shareholders. What the common man gets is inflation and job losses.

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.

All of the things you site are available via search engines, or better handled with expertise so you know how much of the response is nonsense.

Every time I use AI, it's a time waste.

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

I get a lot of correct answers from llms, but sometimes they make shit up. Most of the time, it's some function in a library that doesn't actually exist. Sometimes even the wrong answers are useful because they tell me where to look in the reference docs. Ask it to search the web and cite sources, makes it easier to verify the answer.

I don't appreciate what's going on with AI art and AI generated slop, but the idea that they aren't a useful tool is just wild to me.

Because improving the productivity of every employee by 10% does not translate to the company being 10% more productive.

Processes and systems exist precisely to slow employees down so that they comply with regulations, best practices etc rather than move fast and break things.

And from experience with a few enterprise LLM projects now they are a waste of time. Because the money/effort to fix up the decades of bad source data far exceeds the ROI.

You will definitely see them used in chat bots and replacing customer service people though.

during the industrial revolution(steam/electricity/internet), the world was growing, there're trains, cars, netflix

bussiness grown with productivity growing, even so, we lived through 2 world wars and dozens of economic crisis

but now is very different, when you repair the tank with LLM's help, when the labour value of repairers is decreased, there's no addition value are produced

there's a very simple thought experiment abt the result of productivity growing alone:

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?

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.

We work 35 hour years instead of 35 hour weeks?

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.

I have an AMD minipc running linux that I use for steam gaming, light development, etc. The kernel taint bit is off.

There is one intel device on the pci/usb buses: wifi/bt, and it’s the only thing with a flaky driver in the system. People have been complaining about my exact issue for something like 10 years, across multiple product generations.

If AMD really cared about making money, they would’ve sent MI300s to all of the top CS research institutions for free and supported rocm on every single product. Investing any less than nvidia, the trillion dollar behemoth, is just letting big green expand their moat even more.

That didn’t work out all that well in the medium term (it did in the short term), though it gave them runway to take a big chunk of intel’s server market.

Whether or not that was a good move, it’s not evidence of some engineering shortcoming.

AMD wants it to be them, but the reality is that the moat is wide.

The closest for AI is Apple, but even then, I’m not certain its a serious competitor; especially not in the datacenter.

For Gaming there’s practically no worthwhile competition. Unreal Engine barely even fixes render bugs for Intel and AMD cards, and I know this for fact.

FD: I’m recently holding shares in nvidia due to the recent fluctuation, and my own belief that the moat is wider than we care to believe, as mentioned.

It doesn't take billions of dollars and all human knowledge to make a single human level intelligence. Just some hormones and timing. So LLMs are mostly a dead end. AGI is going to come from differenst machine learning paradigms.

This is all mostly hype by and for investors right now.

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.

But I don't think you can blame users if they're given an immature tool, when it really is on companies to give us a product that is obvious to use correctly.

Its not an exact analogy, but I always like to think of how doors are designed - if you have to put a sign on it, its a bad design. A well designed door requires zero thought, and as such, if "AI" usage is not obvious to 99% of the population, its probably a bad design.

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.

I don’t have any domain knowledge, but I recently saw an executive put in restaurant reservations for five different places the night of our team offsite, so he would have optionality. An article could accurately claim that he later canceled 80% of the teams eating capacity!

But often not.

That was the point in the parent. How this is being reported is bit skewed.

And also there is the problem that nobody reads corrections. Lies run around the globe before the Truth has tied its shoelaces, or some quote like that.

And digging further into the article didn't help either.

If you use gen-AI but don't pay the real cost, you're not really a data point on the economics of it. It could be useful to programmers and still be a terrible business if the cost of giving you that usefulness is more than you (or your employer) would pay.

Speculation:

I suspect after they observed the incredible speed with which Grok was able to build out a leading edge AI infrastructure themselves in Memphis, far faster than what the traditional data-centers could offer it, Microsoft might have had an epiphany and went 'wait, why are we not doing this?'

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.

Siri's success would greatly depend on app developers adopting intents. The major players are going to be hesitant to give Apple that much access to data - the EU may help push them that way, but even still, Microsoft, Google, Facebook, and others want their AIs to be the one people use.

Siri is also limited to Apple products, and while lots of people have iPhones, many of them still have a PC, where Siri doesn't work.

Companies are also very concerned about employees accidentally or purposefully exfiltrating data via AI usage. Microsoft is working hard to build in guardrails there and Intune allows companies to block Siri intents, so Apple would have to do a lot to reassure corporate customers how they'll prevent Siri from sending data to a search engine or such.

But you might be right. I think it's way too early to tell, and that's why so much money is being poured into this. All the major players feel that they can't afford to wait on this.

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?

- Self claiming some AGI milestone is "non-sensical benchmark hacking"

- The AI model race won't be winner-take-all

- The real benchmark is if the world is growing at 10%

MSFT is putting its focus on AI adoption in the app layer and "at-scale consumer properties"

Full conversation: https://www.youtube.com/watch?v=4GLSzuYXh6w

Lots of stupid takes get amplified by people who lack background. Look at the recent Intel/TSMC/Broadcom merger rumors. The story was "Canada could join the US" level of stupid to anyone with experience anywhere near chip fabrication but it still circulated for several days. Also, look at what it did to the stock price of INTC. Lots of money made and lost there.

I think most companies know b2b is the most lucrative segment for AI because it reduces one of their top costs - people. Companies selling AI are basically just selling the ultimate automation tool, which (in theory) is massive value for companies. Having a nice consumer product is a side gig.

- OpenAI and Oracle partnership: When Microsoft is 'at capacity' more demand can go to Oracle, so now Microsoft don't need to rapidly add capacity with leases (which likely have a lower ROI than Microsoft owned and operated centres).

- Longer term investments are still going ahead: Microsoft aren't cutting back on capex investment. They don't want to lease if they don't have to, but long term still see a huge market for compute that they will want to be a key supplier of.

I think Microsoft's goal here is to focus on expanding their capex to be a long term winner instead of chasing AI demand at any cost in the short term. Likely because they already think they're in a pretty strong position already.

> Microsoft’s alliance with OpenAI may also be evolving in ways that mean the software giant won’t need the same kind of investments. In January, OpenAI and SoftBank Group Corp. announced a joint venture to spend at least $100 billion and perhaps $500 billion on data centers and other AI infrastructure.

“ TD Cowen posited in a second report on Monday that OpenAI is shifting workloads from Microsoft to Oracle Corp. as part of a relatively new partnership. The tech giant is also among the largest owners and operators of data centers in its own right and is spending billions of dollars on its own capacity. TD Cowen separately suggested that Microsoft may be reallocating some of that in-house investment to the US from abroad”

IMO it's not a full-on retreat—Microsoft’s still on track to drop $80 billion this fiscal year on AI infrastructure, as they’ve reaffirmed. But there’s a vibe of recalibration here. They might’ve overcooked their AI capacity plans, especially after being the top data center lessee in 2023 and early 2024. Meanwhile, OpenAI—Microsoft’s big AI partner—is reportedly eyeing other options, like Project Stargate with SoftBank, which could handle 75% of its compute needs by 2030 (per The Information report). That’s a potential shift in reliance that might’ve spooked Microsoft into rethinking its footprint.

Also it seems they're redirecting at least some costs - over half that $80 billion is staying stateside, per Microsoft’s own blog, which aligns with CEO Satya Nadella’s January earnings call push to keep meeting “exponentially more demand.” It’s a pragmatic flex—trim the fat, dodge an oversupply trap, and keep the core humming. Whether it’s genius or just good housekeeping, it shows even the giants can pivot when the AI race gets too hot.

And that's the crux of it and that's also why DeepSeek was such a big deal.

I have so much intense hatred for pg and Sama right now I rarely come to this shit show of a site

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."

Bloomberg seems to be a player in the market, or at least it is affected by players in the market.

It always publishes news in good rhythm with the market.

this article is just a reference, still need to wait for Microsoft's announcement

Yes, this is absolutely FUD.

This is the insistence to spread FUD for … some uncertain aim.

Perhaps it hitting all these various firms, in order to leverage their strong reputation, to cause the price to drop, allowing someone - perhaps Bloomberg himself, to make a profit off of it.

No no, I know I sound like a conspiracy theorist now. But my eyes were opened by the sharing of the story.

The fact that FT, WSJ, Fox or a million other sites haven’t latched onto this obvious scheme, is heinous, and once again a sign of our completely captured media.

We can INVENT goals here, to FIT the conclusion we have already reached - that Bloomberg is spreading FUD.

But this is because we have already concluded, and are now finding things to fit our conclusion.

But also iirc; Stargate was well along before trumps horse/pony show also no idea how much is still necessary to purchase.

On the other hand, I think MSFT was trying to pull a classic MSFT on AI. They thought they can piggyback on top of OpenAI's hard-work and profit massively from it and are now having second thoughts, thats better too. MSFT has mostly launched meh products on top of AI.

When the bubble pops you see things collapse

Then it becomes a feeding frenzy as companies and IPs get bought up on the cheap by whoever has a bit of money left

When the dust clears, some old players are gone, some are still around but weaker, some new players have emerged that resemble conglomerates of the old players, but overall a lot of the previous existing power is consolidated into fewer hands