This leads to an over rotation in the perceived value.. the value is significant just as the mobile phone was, but not going to live up to the hype in the near term.
It's definitely interesting how in anonymous forums there's a lot more people pointing out that they think this is hype whereas when we wear our professional hats many of us join in. It's like we all want you to party going no we all know what's going to happen
When your boss is hyping it up and demanding all hands on deck full steam ahead on the Good Ship AI, lots of people join in out of fear, particularly in the currently awful job market which is partly being ruined by AI itself.
Some of us just stay quiet, keep our heads down, and plug away using tools actually fit for purpose, like LSPs and refactoring tools.
Very few have the courage to stand up in a professional setting and say the emperor has no clothes.
https://content.techgig.com/technology/developer-fires-entir...
My spouse works at a large (50k-100k) org in a program management role where she is getting a lot of pressure to organize various AI evangelism efforts aimed at developers. Workshops, bake offs, demo days, etc.
I mean sounds neat, but is this being done because it's useful or because someone up high needs to justify their AI budget spend with AI usage metrics?
Do we believe that ICs are actually so stupid/stubborn they need to be mandated, coaxed, coached, bullied and bribed to use something that makes their jobs easier?
Doesn't most of the best tech end up being bottoms-up?
Most of us who were around 15+ years ago recall a lot of BigCorp had to be dragged unwillingly into mobile by internal useres/devs who got their first iPhone and saw the light. A lot of stuff starts as small team internal skunk works / unofficial projects working around productivity drains. I am highly suspicious that the C-suite knows what people 10 levels down actually need for productivity enhancement.
Your comparison to smart phones is interesting. Smart phones are definitely transformative. There was a lot of hype, but still transformative.
Do you believe that LLMs and AI is also going to be transformative?
What that means is the ad model of the internet will come apart.
And what that means is that the LLMs will need to charge for answer optimization to plug the ads hole.
And so where this is going is basically a whole cottage industry around that. Around controlling and shaping knowledge in other words.
Yes frightening politically more so than economically. At least from my view.
And if it dumbs us down and erodes critical thinking then maybe it will have negative effects economically and politically long term.
Different speeches for different audiences. On HN, for all its faults, people don't need to be told that yes, SOTA LLM can somewhat help you with code, parsing documentation, etc. A lot of people in the "real world" are still grossly underestimating this technology.
Well, when people's financial and employment stability are dependent on placating the overlords who are entranced...
You just summed up machine learning, not just AI/LLMs. My domain is very far from LLMs, but even in my domain, you can build a really cool demo, that is entirely misleading.
So far the Self driving car hype cycle has served as a useful reference for understanding the hype with LLMs. The main difference with LLMs is that there is 10 times as much money flying around.
I think there’s too much hype and money, but LLMs are useful right now.
People are still figuring out very basic integrations, and even now, at this early stage, the things I can do with LLMs are pretty incredible. For example, I was able to set Cursor up on finally dragging an old codebase out of the dark ages. It then built new features that I've long wanted. It took a few hours on my end, but would have been at least a week or two without it.
I'm not exposed to much of the hype, though, so maybe my calibration of what the hype is, is wrong.
Such as?
Just look at what Cursor (and similar) have done in terms of the tooling for LLMs. There's still tons of progress to be made there, but similar tooling can happen across a variety of industries and categories.
For example, I run a database of information that needs constant updating. I set up automated fact checking (with a human looped in), that enables nearly live updates, which would be incredibly expensive without an LLM. There are so many projects, big and small, just like that one, that are being created right now. The low hanging fruit is extremely abundant, for those who are able and willing to find it.
On the other I get 5 hours of work done in 5 minutes every other day.
Worst I can see happening is a doc-com crash. pets.com will go out of business, but amazon won't.
Oil isn't "free" anyway, it takes energy to make energy, and EROEI has been going down for some time as the easy oil is extracted.
Even if every major company in the US spends $100,000 a year on subscriptions and every household spends $20/month, it still doesn't seem like enough return on investment when you factor in inference costs and all the other overhead.
New medical discoveries, maybe? I saw OpenAI's announcement about gpt-bio and iPSCs which was pretty amazing, but there's a very long gap between that and commercialization.
I'm just wondering what the plan is.
Oh and John Carmack, of Doom fame, went off to do AGI research and raised a modest 20(?) million last I heard.
“Somebody” like… Sam Altman? Because he said that’s what he actually believes.
https://www.startupbell.net/post/sam-altman-told-investors-b...
Think of it as maybe $10k/employee, figuring a conservative 10% boost in productivity against a lowball $100k/year fully burdened salary+benefits. For a company with 10,000 employees that’s $100m/year.
New co's built by individuals who get AI are best positioned to unlock the dramatic effects of the technology, and it's going to take time for them to eclipse encumbent players and then seed the labor market with AI-fluent talent
But rather than speculating, I'm generally curious what the companies are saying to their investors about the matter.
We're not even at AGI, and AI-driven automation is already rampaging through the pool of "the cheapest and the most replaceable" human labor. Things that were previously outsourced to Indian call centers are now increasingly outsourced to the datacenters instead.
Most major AI companies also believe that they can indeed hit AGI if they sustain the compute and the R&D spending.
What it isn't is the actual final "thing" itself. It's just the thin veneer right now.
I'm not convinced that that revolution was worth whatever trillions we'll end up spending, but fortunately that's not on my shoulders to be worried about.
Apparently the total market capitalisation of the US stock market is $62.8 trillion. Shiller's CAPE ratio for the S & P index is currently about 38 -- CAPE is defined as current price / (earnings, averaged over the trailing 10 years)
That suggests that over the last 10 years, the average earnings of the US stock market is about $1.7 trillion annually.
So $344B of spending is about 1/5 of the average earnings of the total US stock market.
Still hard to interpret that, but 1/5 is an easier number to think about.
> This year the world’s four largest tech firms will spend $344 billion on AI
> Altogether, the four companies are expected to spend more than $344 billion for the year, with much of it going to the data centers necessarily to run AI models.
so both articles frame that $344B as estimates of capex within 1 year.
If one would assume it's nearly all a bubble, How would you correct earnings for the US? I am interested in applying it to any investment that tracks AI heavy companies in the US.
One approach I've seen a few folks do is to fit a regression model of annualized real stock market returns over the next 10 years as some function of CAPE or 1/CAPE or log(CAPE).
It doesn't give a very good fit on training data, R^2 in the range of 0.2-0.3, i.e. it cannot "explain" most of the variation in 10 year returns.
CAPE based regression models like that have said the US stock market has been overpriced for the last decade! But investors in the US stock market have done pretty well over that period, with really good returns. Maybe these models are accurate but we've just gotten lucky? Maybe these models aren't very good. Hard to tell.
Elm capital publish estimates of expected returns of a few asset classes quarterly: https://elmwealth.com/capital-market-assumptions/
LLMs risk most of those companies business, they can't afford to not be ahead. If they aren't ahead, there's a risk that the entire US's economy would be in a terrible shape.
American Big Tech companies that make plenty of INTERNATIONAL revenue from Ads (Meta, Google), can quickly become a shell of its former self.
How? Countries and economic blocks could quickly substitute their American products counterparts if they have nothing to offer and could roll out their own.
The US's economy has become very dependant on FAANG cashflow, it's what gets other parts of the economy moving.
No wonder they had a dinner with Trump. If this fades away, US will look very weak and with a terrible economic outlook.
LLMs are a million times better than Crypto currencies.
It’s just none of that had any baring on the value of the coins.
I don't even care about multimodality etc. I think pure text models are a very appealing idea.
redwood•1h ago
The Economist recently featured a piece pointing out that it's no longer risk that drives the market but a balance of fear of loss and fear of missing out (https://www.economist.com/finance-and-economics/2025/08/06/w...). FOMO is out of control right now
jakeinspace•1h ago
brookst•57m ago
andsoitis•47m ago
fidotron•1h ago
Exactly. The whole stock market is currently behaving like the crypto bubbles.