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How the AI Bubble Bursts

martinvol.pe

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Re: How the AI Bubble Bursts

#281

Post by pydry »

adventured wrote: post #234
pydry wrote: post #153

Earlier quoted context omitted.

Jevons paradox only applies if demand hasnt already been saturated. The fact that public LLM usage is leveling off at a price of $0 and Jensen "we make the shovels in this gold rush" Huang is rather desperately claiming that you need to spend $250k/year in tokens to be taken seriously suggests that demand saturation may not be that far off. Whether Jevons' Paradox applies to software engineers I think is another open…

LLMs haven't remotely begun to be integrated into the lives of the typical person. Not even close. The typical person is using LLMs not at all as it pertains to their daily life tasks. They're using them almost entirely for limited discussion matters (eg having a discussion with GPT about a medical issue, or a work related matter). This is the first or second inning in the LLM rollout. It'll take 15-20 more years for…

>The typical person is using LLMs not at all as it pertains to their daily life tasks.

This doesnt track at all with my experience. Everybody is using it everywhere.

Moreover people are using them for daily life tasks even when it is not an appropriate use of LLMs - e.g. getting medical advice as you referred to or writing emails which are clearly pissing off their coworkers.

In this respect I see it as akin to radium - a new technology that got a little too fashionable for its own good when it first emerged and which will likely have many use cases scaled back.

Re: How the AI Bubble Bursts

#282

Post by sooperserieous »

tapoxi wrote: post #122
piker wrote: post #31

> They lose a big customer for their cloud services. Even worse considering that now, using the AI they helped fund, everyone can compete with their sub-par products. GitHub is a good candidate for disruption, and that’d be just the start. Look, I'm a Microsoft hater like the rest of us, but calling Microsoft's products sub-par discredits the author a good bit. I invite anyone who thinks this to try and compete with…

The state of GitHub and Windows 11 certainly qualify as sub-par.

I think Github represents 'par'. Plenty of stuff worse and plenty of stuff better. Overall it's what most people expect a coding social media site to be because it set those expectations. Those of us who are only looking for code management (including issues/PRs/etc) are easily satisfied elsewhere.

Re: How the AI Bubble Bursts

#283

Post by wavemode »

upcoming-sesame wrote: post #243
wavemode wrote: post #163

Earlier quoted context omitted.

people used to say this about search engines and web browsers, as well regardless, eventually Google became the universal default for both. When it comes to software, the average person doesn't shop around for the technologically optimal choice, they just use what everyone else is using.

Google search is free to use. if they spike the models price up, people will look for alternatives

AI (that is, plain chat) is always going to be free to use as well. Google and Microsoft are going to keep it that way. And make the money back via ads.

That's why ChatGPT still has a free option. If they didn't, they would lose a billion users overnight to Gemini.

Re: How the AI Bubble Bursts

#284

Post by layer8 »

beepbooptheory wrote: post #59
baq wrote: post #32

Earlier quoted context omitted.

If they shut down all training today they’d be absolutely printing money for the next couple quarters and then die with a bang once the other lab releases the next frontier to the public.

I don't really get the last bit. It's hard to imagine what a new fangled "frontier model" could do that would blow anyone out of the water. Like what does this look like? Really good benchmarks? Who cares about that anymore?

Not hallucinating anymore would be a good start.

Re: How the AI Bubble Bursts

#285

Post by fcarraldo »

rileymichael wrote: post #189
fcarraldo wrote: post #162

Earlier quoted context omitted.

Well, not GP, but I do. Let’s look at the numbers: Median senior SWE salaries in SF: https://www.levels.fyi/t/software-engineer/levels/senior/loc... Median income in metro areas: https://www.cnbc.com/2024/07/11/the-median-salary-for-the-25... Engineering salaries are significantly higher than nearly every other industry on average and on median. Much of this is driven by VC funding rather than sound, profitable, boot…

> Engineering salaries are significantly higher than nearly every other industry on average and on median now compare the profit per employee at tech (software engineering) companies and those industries..

At the top end (say, top 100 tech companies) it’s pretty high indeed. Public companies, for sure, as otherwise their stock price would tank. It’s not uncommon in this industry to have margins above 70-80%.

But there are thousands if not tens of thousands where the profit per employee is minimal or negative.

I can’t find a source for all tech (the data wouldn’t exist for private firms anyway) but I think it’s telling to look at this list, scroll down to about the middle and look around at salaries you or your colleagues are pulling. Software revenues are certainly high but the industry is afloat because of these high margin businesses creating returns so that low margin businesses can exist. Without the massive infusion in upfront capital, very uncommon in other industries, it’s simply not sustainable.

Typically a market that’s buoyed by its top performers but has significant amounts of capital tied up in under performers is called “a bubble”.

https://www.trueup.io/revenue-per-employee

Re: How the AI Bubble Bursts

#286

Post by raincole »

pydry wrote: post #153
amelius wrote: post #97

Earlier quoted context omitted.

> 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. I think it is determined: https://en.wikipedia.org/wiki/Jevons_paradox

Jevons paradox only applies if demand hasnt already been saturated. The fact that public LLM usage is leveling off at a price of $0 and Jensen "we make the shovels in this gold rush" Huang is rather desperately claiming that you need to spend $250k/year in tokens to be taken seriously suggests that demand saturation may not be that far off. Whether Jevons' Paradox applies to software engineers I think is another open…

It is quite hard to imagine how the demand is saturated now. I think any company that uses a sliver of AI will happily increase their token consumption 100x if it's free.

Re: How the AI Bubble Bursts

#287

Post by coldpie »

infecto wrote: post #182
iterateoften wrote: post #93

Earlier quoted context omitted.

According to open router token demand is growing at something like 10% a week It’s insane

I wish this was higher up. I have been tracking the same since Thanksgiving ‘25 and the growth is unreal. Again I don’t know where the cards fall maybe the industry overspent on capex but it’s at least easier to see why they are spending based on demand. The risk of being left out is greater than overbuilding.

I do wonder how much of the apparent demand is driven by companies automatically running these things when users didn't actually ask for it. For example every web search I make now has an AI response that I scroll right past. I'm sure that counts for someone's token usage data, but I got zero value from it. This is happening in almost every software product now.

Re: How the AI Bubble Bursts

#288

Post by marcyb5st »

fcarraldo wrote: post #162
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.

Well, not GP, but I do. Let’s look at the numbers: Median senior SWE salaries in SF: https://www.levels.fyi/t/software-engineer/levels/senior/loc... Median income in metro areas: https://www.cnbc.com/2024/07/11/the-median-salary-for-the-25... Engineering salaries are significantly higher than nearly every other industry on average and on median. Much of this is driven by VC funding rather than sound, profitable, boot…

Isn't salary a proxy of how hard to replace one person or a group of persons is or how valuable they are?

There was a surge in demand for SWEs and scarcity brought salaries up. Are them too high? Hell no. On average, my colleagues and me generated ~2M$ each in 2025 for our company, while we get payed a fraction of that (grants and bonuses included). If you look at net income per employee we are at around 700k each in 2025.

Additionally, employers try their hardest to drive costs down (eg. offshoring as much as possible, everyone doing layoffs at the same time, ...) and average/median salaries remained high. If the salaries were overinflated those numbers should have came down I believe. The fact that they didn't makes me think that it still is a scarcity problem not an overinflation one.

Re: How the AI Bubble Bursts

#289

Post by functional_dev »

aurareturn wrote: post #199
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…

Even if TurboQuant, which was released a year ago, drastically lower RAM requirements, AI labs will just release bigger models. Jevons Paradox. When are we going to learn that efficiency gains in AI does not decrease hardware usage?

valid point, it reminds me of video games. GPUs got faster, devs pushed higher resolutions, more complex lighting instead of saving power :)

Re: How the AI Bubble Bursts

#290

Post by drakythe »

infecto wrote: post #264
drakythe wrote: post #247

Earlier quoted context omitted.

3.99 at 8x instances, with a minimum 2 week commitment. Good luck getting 70% usage average during that time. Useful when you're running a training round and can properly gauge demand, not so great when you're offering an API.

Is it not a good penciled number? It helps set the directional tone that at inference cost is being covered.

It says the numbers are theoretically possible. Requiring a 66% usage to break even when 100% usage will piss off customers by invoking a queue means it’s a balancing act.

“Technically correct. The best kind of correct”. So inference may technically be _capable_ of being profitable, but I have question’s about them being profitable in _practice_.

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