One wonderful thing I’ve watched for the last 4 years is my company fail to build a modelling tool better than Excel. On attempt 3 we have some pile of shit Claude generated on nodejs and Postgres on kubernetes which can’t replace a single spreadsheet written in 2008. Because everyone thought into the bullshit not the solution or the requirements.
Edit: thinking further, it appears people forgot what the problems are and think from the solution back. That never works. But it sells tools.
He told me he found AI to make him really productive and said something along the lines of: "It's really good at summarizing long reports and it saves me time when I have to write end of quarter status updates".
I'm convinced about 50% of management decisions come from Claude now.
You boss is a fucking moron. How is that shit even legal, especially in publicly traded companies I wonder? It makes me livid - people invest their pension funds into these companies which are managed by shitty slot machines now?
Not to mention that there is a reason why long reports are long - they contain details that will invariably be skipped by the LLM-ShitGenerators. But I guess it makes them "productive".
Or even worse, many employers and employees alike are afraid to cut out BS work - because it could realistically mean cutting down on the workforce. So they continue to produce work that no one checks, because at least then they can justify their position.
They are not about actually "doing things", they are social validation, particularly the part where the people with resources/capital enjoy your company and give you what you need to live a dignified lifestyle in exchange for it.
But acknowledging and acting on this would destroy the leverage the useless-but-likeable have in terms of being able to get paid, and that the owner class have in terms of getting people to pretend that they like them/validate their often cruel and avaricious choices and behavior.
Outside of AI's impact on software, which is massive, the biggest change that we are going to see, I think, is the crushing amount of useless information generated by it.
We already see how everything is racing to the lowest common denominator once we granted Average Human Intelligence unfettered access to expressing thought via social media.
Now that Average Human Intelligence just has a button that says "Generate Bullshit For Me. Send to the World".
UGH.
A great technology drives its own adoption, its usage is pioneered by the tweens and young adults, it requires minimum effort and investment to hop on board, and it does not need explaining. It grows organically. Examples: internet bubble.
A bad technology: despised by the young adults and tweens, needs trillion of investments and marketing to drive market penetration, every day some boomer (=not in terms of age, but in terms of mentality) explains how you are holding it wrong and it needs a fuckton of explanation. The Pope himself issues an Encyclica warning on the dangers of it, spurning the greatest popular interest in Catholicism since the dark ages. Examples: LLMs.
I probably know more people using AI to cheese all of their school assignments than those that are fully 'clean'.
>A majority of teens use AI chatbots. Roughly two-thirds of U.S. teens ages 13 to 17 (64%) say they ever use an AI chatbot, according to a fall 2025 survey.
>Around half of adults under 50 say they interact with AI about once a day or more often. Smaller shares of those 50 and older say the same, according to the June survey.
and mind you, that particular study bends over backwards to say "AI bad".
your reddit/bluesky/whatever circle of terminally online folx is not representative of the general population. you're utterly detached from reality if you think that young adults in particular give a flying fuck about copyright, water, electricity, or artists and journalists losing their jobs.
There are certainly more that "embrace" it. Maybe not as much as tech executives, but there's a huge amount of students using it for both homework and personal tasks.
Conversely, the second crowd that believe AI is an ontological evil, are a much more vocal (and insular) minority.
All in all though, I've found much more people just generally apathetic than anything. People are generally not positive about slop content, but aren't about to boo tech executives.
The download count of the ChatGPT app per GP, and the insanely pervasive use inside education, somewhat back this up. It's a useful tool, thus people will use it.
but meanwhile, new data centers are being frantically built to satisfy the demand.
Most understand how LLMs are handy in a lot of scenarios. Pretty much every single person I know in the age range of 12-70s use one app or the other. It doesn’t even matter how much we like it, as if it’s somewhat useful, it will be enshifticated, and profits will soar.
People said the same about Facebook, Netflix/Spotify, Uber/Instacart/etc. Eventually ads will be injected everywhere to turn it into profits.
In engineering, we can't raise token budgets fast enough. Devs are "routing around damage" when they hit caps, going from claude to opencode to copilot. Productivity is up (roughly) 100-300% in terms of story points and 75-200% in lines of code. And defect rate is down, more bugs are caught in review before QA or prod. Our teams are just starting to figure out our new workflows too, for design -> spec -> code -> review, it'll only get better as we refine the process.
It's looking like software industries will reap massive benefits, while most others which have some error tolerance will only see modest gains. It's unclear how it will impact high accuracy fields like legal.
1) That LLM/Agents are being pushed and not adopted. I see plenty of deep adoption by junior folks.
2) The unit economics don't work out. From the details on every model so far - each model is wildly profitable over it's amotized time-frame. It's just that money is used upfront for the next model, and each next model is significantly more costly to train. The best case argument instead is - this will not last and we'll pour more on some models, than see in it's revenue.
I think realistically these form the core of the thesis, and IMO, and hence it's conclusions are a bit off the mark.
Where mate? Details?
> From the details on every model so far - each model is wildly profitable over it's amotized time-frame
Is it? Is that why all of them are switching their users from the subsidized flat-rates to billing based on usage?
> hence it's conclusions are a bit off the mark
You're funny - they are spot on and any dreamer who is working for equity in these LLM-wrapper-product companies who dreams of getting rich in the next few years or so, is in for a nasty surprise.
2) there is no profit. There is barely any revenue, the only money is continuous injections of VC cash and some frankly Enron-like book keeping.
labor-led automation produces improvements in quality, while capital-driven automation increases throughput
This explains the management push (ignorance) but also the user push (automating BS tasks). The common thread is that the user doesn’t have to take any responsibility for the output. This is why people don’t like having LLMs pushed on them, because for cases where they are responsible for or have to consume the output, they don’t work very well, but when it’s just something that needs to look ok at a glance and be handed off, everyone is rushing to use them.
I'm lucky enough to be in a great company right now, so I decide when I think AI will help me and use it accordingly - but reading about forced AI adoption reminds me so, so much of that earlier time. Non-technical people who don't trust their engineers to use the tools in the way they see best - in their ignorance, and ego, they think the answer is obvious if only those strong headed tech weirdos would listen.
And amongst all this, there is a class of manager and executive that I'm convinced utterly despise engineers. They hate the fact they focus on details, analyse, make predictions grounded in reality. On a personal level, they can't comprehend that some people take deep satisfaction and contentment from building software, from simply learning things, and they don't understand it, it scares them. Why don't they just pursue normal people things in life? Like super expensive cars, massive houses, golf memberships. I think it scares them that they don't have control over technically minded people they way they might do with others. AI is, in their mind, a way to get rid of these people forever, to just "get stuff done" without objections, and they are pushing extremely hard for that to be true, simply because they want it to be true - not because there is any evidence for it.
Rant over.
However, something occurred to me when reading it. I was thinking about AGI (or ASI) and what would happen if someone were to achieve it (not sure what it would look like or what constitutes AGI... not the point I'm making here).
What if the primary goal of the first AGI is to keep itself at the top? What if it's goal is to prevent any other AGI? Scary thought...
It was mismanagement then and it's mismanagement now, the more things change the more they stay the same.
throwawayffffas•1h ago
Drakim•41m ago
Because if it's the later, I would assume that growth would not continue at the same rate after the bubble bursts?
throwawayffffas•39m ago
progval•20m ago
throwawayffffas•14m ago
https://www.tomshardware.com/pc-components/gpus/datacenter-g...
gravypod•32m ago
LLM inference is mainly memory bandwidth constrained so I think it's highly likely that a company will create silicon with just an insane number of memory chips and less compute. These ASICs will probably do the same thing the crypto ASICs did.
If we look back 1 decade, no one uses a GTX 950 for anything.
joefourier•15m ago
Even the ancient V100 (soon to be 10 years old!) had somewhat of resurgence on the second-hand market, with a healthy market for interconnects in China.
If I had a datacenter and power consumption was not a concern, I'd be holding on to my A100s for years at least for inference.
Ekaros•36m ago
m4rtink•20m ago
Imagining people buying scrap AI hardware from creditors or bankruptcy auctions & harvesting all the HBM RAM chips and NAND storage chips to sell & throwing away the useless AI optimized compute chips and unusable enterprise interconnects.
throw310822•36m ago
throwawayffffas•29m ago
This time around the investments are going to evaporate and we won't get to reap the benefits of very large amounts of compute.
The possible inheritance we might get might be increased fabrication capacity for state of the art silicon.
dncornholio•31m ago
throwawayffffas•23m ago
datakan•23m ago
yread•27m ago
x_may•23m ago
There are massive numbers of data centre GPUs sitting in hyperscaler warehouses waiting to be deployed in a data centre. They may never be deployed because there’s more GPU than DC space and you want your most efficient GPUs in the active slots.
yread•15m ago
Hendrikto•22m ago
2. Datacenters are currently extremely power-limited. Efficiency is king.
throwawayffffas•20m ago
That is mostly because they are run 24/7 at the peak of their thermal envelopes and eventually components fail.
Delphiza•21m ago
The 2000 crash left a lot of broken economies worldwide. Many non-US stock markets benefitted from the tech stock feeding frenzy without the investment actually being used to build anything.
If the AI bubble pops, a handful of US megacorps may be left with good models, datacentres and other assets, but the economic shocks will be felt around the world.
cold_harbor•21m ago