Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

How the AI Bubble Bursts

martinvol.pe

Post reply on HN
Print view View original

251–260 of 557 posts

Re: How the AI Bubble Bursts

#251

Post by vekker »

hirako2000 wrote: post #117
Aperocky wrote: post #58

Earlier quoted context omitted.

Demand of tokens is absolutely skyrocketing. And unlike the traditional "this will replace humans right away", I think what this introduce is a lot of incentive to spread those token in places where there was never any incentive to hire a software engineer for previously. In turn, that will drive a lot of business activity in those area that will potentially fail given the current quality of the output. This feels li…

Tulips sales also skyrocketed. Seriously, what value are tokens providing other than justifying layoffs. Concretely. Today. Not in the speculating scenario that cardiologist could be replaced with models. We see this new trend of agentic coding, again a promise software will be written that way going forward, despite the number of fiasco already experienced when trusting a model turned bad. The use case may provide v…

> Seriously, what value are tokens providing other than justifying layoffs

Like the OP said, it's incredible how polarizing this debate is. When I read comments like yours, I feel like a significant part of the global workforce in IT must be living on another planet? Or they never really used Claude Code, Codex, OpenCode, ... intensively before because of company policies?

I legitimately am at least 10x more productive than a year ago, and I can prove it in number of commits and finished monetizable features developed per day. Obviously my workflows still very much require an active, constantly context-switching human-in-the-loop, but to me there's absolutely no question both output volume & quality have skyrocketed.

Re: How the AI Bubble Bursts

#253

Post by 9rx »

SirensOfTitan wrote: post #226
9rx wrote: post #154

Earlier quoted context omitted.

> This is a really concerning perspective: people were paid what they were worth. The parent comment doesn't discount that, only pointing out that "what they were worth" was inflated due to a speculative environment. Wherein lies your concern?

I think calling it inflated is to play to a narrative that labor was overvalued broadly in tech. Salaries across industries in the US have remained flat since the 1970s. Calling the one sector that can provide access a middle class lifestyle inflated s to play into a narrative capital is eager to tell, even if OP didn't intend that.

> Salaries across industries in the US have remained flat since the 1970s

What do you mean? The real (meaning adjusted for inflation) hourly wage in the US has increased by around 20% since 1970.

What has changed since the 1970s is that wages are no longer coupled to productivity. Perhaps that is what you are thinking of? But that should be an obvious truism for anyone in tech. We create the very things that cause that to be the case!

Re: How the AI Bubble Bursts

#254

Post by scw »

fotcorn wrote: post #84
joshstrange wrote: post #53

> RAM prices are crashing because new models won’t need as much Reality begs to differ [0] and following the link for that text goes to an article [1] where they talk about Google's TurboQuant which supposedly will lower the RAM requirements. Now if that means RAM prices come down (as speculated, not reported on, in the link) or the AI companies just do more things with their extra ram is yet to be determined. The fa…

Also, there is zero reason to think that the big labs did not have anything similar to TurboQuant for a long time already. The recent blog post from Google announcing TurboQuant does not change anything regarding RAM planning for the big labs. TurboQuant itself is already a year old! So even smaller labs have probably seen and implemented it.

TurboQuant has a specific benefit by compressing the KV cache at a negligible cost to quality. That mainly means that the context lengths can go up in models for the same amount of memory, however the KV cache only accounts for something like 20% of the overall model size, and this will not dramatically decrease memory demands in the way that some of the more sensationalist reporting has stated.

Re: How the AI Bubble Bursts

#255

Post by gruez »

schmidtleonard wrote: post #135
chrisweekly wrote: post #100

Earlier quoted context omitted.

> "decades of overinflated engineering salaries" 'Overinflated' relative to what? You make some good points but I don't accept this as a premise.

Overinflated relative to the wet dreams of the ownership class.

It's not exactly stuff of "wet dreams of the ownership class" to say that of the possible white collar careers, software engineering is pretty hard to beat in terms of salary vs work you need to put in.

Re: How the AI Bubble Bursts

#256

Post by naravara »

malfist wrote: post #209
naravara wrote: post #113

Earlier quoted context omitted.

I think they’re losing money because they have to amortize the costs of training the models in the first place, which is where most of the resource sink is. This is why they were freaking out about DeepSeek just taking the trained model weights and slapping an interface on it.

Thats like saying a restaurant is profitable because they're making money selling meals if you ignore the costs of ingredients. Of course they are profitable if you ignore their cost to bring a product to market.

That’s the wrong analogy. Model training is more like the setup costs of developing the menu and training staff. What’s driving the costs is important when talking about financial sustainability. If it’s mostly coming from optional R&D investments instead of the direct costs of producing the food then you can simply not exercise the option and be profitable. If it’s more coming as a variable cost that scales with each meal served that’s a very different situation.

Yeah it should be factored in, but it’s a different set of implications for long term sustainability. They don’t actually need to test and optimize a new menu every day or week. If they decide to just stick to the same one longer they can get way more return from each dollar spent on development. It’s just that right now the rate of improvement you get with training is really high and nobody can afford to fall behind their competition.

Re: How the AI Bubble Bursts

#257

Post by skeeter2020 »

sempron64 wrote: post #173
hirako2000 wrote: post #117

Earlier quoted context omitted.

Tulips sales also skyrocketed. Seriously, what value are tokens providing other than justifying layoffs. Concretely. Today. Not in the speculating scenario that cardiologist could be replaced with models. We see this new trend of agentic coding, again a promise software will be written that way going forward, despite the number of fiasco already experienced when trusting a model turned bad. The use case may provide v…

It's ridiculous to call this tulips, in the sense of a speculative asset whose price depends on resale. A more similar recent example is the dotcom boom and bust based on building internet infrastructure, or the 2008 crash which was based on cyclical infrastructure overinvestment. These crashes were characterized by demand growth not keeping up with investment because the target markets were tapped out. Not clear whe…

dotcom was maybe 100B a year focused on the US and mostly VCs. AI is perhaps 250B global VC (with more than half of ALL VCs concentrated in one sector) and another 800B+ from non-VC. These numbers are basically a guess but structurally we are set up for something much, much worse.

Re: How the AI Bubble Bursts

#258

Post by wongarsu »

ZitchDog wrote: post #242
wongarsu wrote: post #166

Earlier quoted context omitted.

I can get Kimi K2.5 inference on openrouter for about $0.5/MTok input + $2.5/MTok output, from six providers that have no moat besides efficiently selling GPU time. We can assume they are doing so at a profit (they have no incentive to do this at a loss), giving us those numbers as the cost to serve a 1T-a32b model at scale. Now we don't know the true size of any of the proprietary models, but my educated guess is th…

> they have no incentive to do this at a loss Are you sure? Surely there is a lot of interesting data in those LLM interactions.

Many of them are promising not to store any of this. Of course we have to trust them, for all we know they are all funded by various spy agencies

Re: How the AI Bubble Bursts

#259

Post by infecto »

malfist wrote: post #209
naravara wrote: post #113

Earlier quoted context omitted.

I think they’re losing money because they have to amortize the costs of training the models in the first place, which is where most of the resource sink is. This is why they were freaking out about DeepSeek just taking the trained model weights and slapping an interface on it.

Thats like saying a restaurant is profitable because they're making money selling meals if you ignore the costs of ingredients. Of course they are profitable if you ignore their cost to bring a product to market.

The problem with that comparison is restaurants largely don’t have much room to adjust price or optimize cost. The AI industry is too new with many unknowns right now so investors are willing to take risk. For the hyperscalers the bet is that being left out is going to be a greater loss than overbuilding.

Re: How the AI Bubble Bursts

#260

Post by h14h »

Tade0 wrote: post #161
infecto wrote: post #30

It’s incredible how polarizing the AI rush is. I keep the perspective that the technology is an absolute step change but I have no idea where the cards will fall. I take a lot of issue with these style of articles. I get a sense that the authors are being overly defensive. The cost to serve tokens is absolutely profitable today and that’s been true for at least a year. What’s unclear is how R&D and capex fit into the…

My main worry is - once this is all over, the market consolidates and using LLMs will become a requirement in job listings, what's the highest price per million tokens companies will be able to charge us? Currently on a given day I'm chewing through approximately the equivalent of my lunch money, but where there's opportunity to extract wealth, someone will find a way to do it.

My (potentially naive) take is that open models will save us. The biggest markets for LLMs (e.g. coding) are narrow-enough to be served well by smaller models with proper RL. Cursor's Composer 2 (created from a Kimi K2.5 base) is a great example, and I expect it to be the first of many.

The wealth of great open models provide an excellent base for fine-tuning, distillation, and RL. I see a lot of untapped potential in the field of bespoke, purpose-built models that can be served far more cheaply than the frontier competition. I would not be surprised if we see frontier-adjacent experiences running comfortably on a Mac Mini by year end.

With frontier models seemingly hitting diminishing returns in quality, I struggle to see a world in which gigantic, expensive, general-purpose models don't become increasingly niche.

Post reply on HN