Earlier quoted context omitted.
I don't necessarily buy that API prices represent "real" prices of the models.
Yes, they are ludicrously profitable - even when future training costs are taken into account!
[citation needed]
After the AI Crash
171–180 of 244 posts
Re: After the AI Crash
#172Earlier quoted context omitted.
> Firstly, AI is not going to take away all jobs Human-level AGI is by definition able to take away all human jobs. > Secondly, we already know what happens when sectors face mass unemployment from the 1980s, when steel and coal workers were laid off en-masse: nobody came to help There is a massive difference between 20-30% of people in a country being unemployed (historical sector-wide collapses), and 80-100% of peo…
> 80-100% unemployed This would mean that there are no AI companies at all, because there is no one to buy their products. So, they have nowhere to get money for whatever they want to sell.
its basically the end of capitalism... for better or worse...
Re: After the AI Crash
#173Earlier quoted context omitted.
> what is going to happen to the job market during and the years following the crash Junior programmers will be in demand again. Someone will have to fix the mountains of vibe coded technical debt.
I've already seen an instance of a junior developer being completely unable to fix vibe coded technical debt, even with guidance, because AI analysis is fundamental to how they understand code and they're unable to comprehend what's happening when the AI analysis is not right.
i see this every day in the field with new juniors... they cant fix anything that would be a 1 line change if they could actually read the code but instead spend hours churning the ai to get the answer... if thats all they could do i'd never hire someone like that personally but companies with lots of margins seem to be able to absorb this for now... idk
Re: After the AI Crash
#174Earlier quoted context omitted.
> Firstly, AI is not going to take away all jobs Human-level AGI is by definition able to take away all human jobs. > Secondly, we already know what happens when sectors face mass unemployment from the 1980s, when steel and coal workers were laid off en-masse: nobody came to help There is a massive difference between 20-30% of people in a country being unemployed (historical sector-wide collapses), and 80-100% of peo…
> 80-100% unemployed This would mean that there are no AI companies at all, because there is no one to buy their products. So, they have nowhere to get money for whatever they want to sell.
at human-level AGI there would be no need for people to 'buy' when the machines can just do
Re: After the AI Crash
#175> analysts have estimated that it will take $2 trillion a year in revenue to pay for the infrastructure that has already been built I doubt any credible analyst has claimed that. What's the total AI capex that's already been spent? About $1T? It's a pretty absurd idea that those DCs need to make $2T/year for 5-7 years -> $10T-14T over their lifetime to break even. (Yes, this is nitpicking in the sense that there are…
The post is just full of half-baked conjectures masquerading as facts... combined with "things they read" by unspecified authors and sources... the author seems to prefer engaging in AI doomerism as opposing to actually understanding.
> Diseconomies of Scale. Every new technology I can think of thrived, in part, due to economies of scale, where the larger the industry grew, the more efficient it got. AI is going in the opposite direction, where every new AI model consumes more resources than its predecessors. This may turn out to be the fatal flaw – the bigger the industry gets, the more its operating costs increase.
[Agreed] Newer gens of models are more power-efficient per task, not less.
This is a low quality post full of basic errors.
Re: After the AI Crash
#176> Most new technologies have been welcomed by the public with open arms. I'm not going to predict how this is going to turn out in either direction but this statement gives me pause. I don't think that's ever been true. Yes, the siren song is strong but initially most new tech is met with skepticism. Are we so quick to forget "the internet/computers are just a fad"-type thinking?
The post is overloaded with false generalizations like this.
Re: After the AI Crash
#177I've been reading a lot of stories like this lately. I'm no business genius, but you'd assume that investors are. Are they just blind or are they burning cash on purpose. What's the steelman argument here?
Looking at these investments through the lens of traditional businesses (which is the perspective taken by articles like this) won't make sense. It's not until you appreciate the expectation of how disruptive this technology will actually be does any of this make sense.
These people think they're on the verge of creating a technology which, at a minimum, would constitute an unprecedented superweapon (and, at the extreme, would usher in a new era of civilization). Even if you don't buy-in to the take that one of these companies will reach a singularity and create a superintelligence, the cybersecurity implications alone is enough to put these products into a category outside the confines of profitability. We're already starting to see these implications become reality.
If the US NEEDS an advanced AI on an existential level, then it doesn't really matter how much it costs to make or whether or not it can produce a profit. It'll be valuable one a scale where financials like that just don't apply.
Re: After the AI Crash
#178Earlier quoted context omitted.
How would one position oneself as an individual investor if one believed this thesis?
Be careful if you expect a dot com fallout - that was retail investor driven and took a long time to unwind as people sat through painful drops. Ai is much more of a private investment bubble. AI remains useful. What is likely to go away (and all at once) is investment and free rides/discounts. So I expect more of a sobering process for AI companies rather than a blowup, simply because they all still will have cash i…
Unlike the dotcom era, infrastructure is being built by mature highly profitable companies with broad product portfolios to exploit future trends and needs... Data-centres or even their power/water contracts seem like something with a lot of value even if we take a cynical view of LLMs profitability.
Am I overstating the case? My understanding is that a data center tends to be ‘purpose built’, so they may need a gutting for repurposing, but assuming a lot of ‘sobering up’ I’m envisioning several giant cloud providers with excess capacity and a scaling potential.
Be it Jevon’s paradox sparked by cheap compute, another huge tech fad, or a ML breakthrough that brings another kind of model to the forefront, we’re likely to want a lot of compute at some point. It doesn’t seem like bad long-term positioning for the tech giants or investors.
Re: After the AI Crash
#179It’s not a question of if but when at this point. To parallel to The Big Short this is the point in the movie where folks realize it’s mathematically impossible for things to not implode and so players are quietly positioning themselves for that eventuality before things are allowed to blow. It’s been a dramatic shift these last six months but everywhere I look now folks are quietly preparing their battle armor to su…
How would one position oneself as an individual investor if one believed this thesis?
Forge a sell-all trigger and test it several times with just a minimum level transaction. Worst case is you sell a day or two ahead of the full pop.
Re: After the AI Crash
#180AI investment will crash but AI itself (the technology) will continue thriving, learning, improving and there is absolutely no way to stop it. The only reading on the crystal ball is if US companies fail, China will take the lead by leaps and bounds, so the only solution is to keep pushing the cart until the wheels come off or we all cross the finish line, together.
It can't 'learn' on its own. Models only get better with mountains of RnD for data, training, and lots of fine tuning. So the moment investment dries up, models stop improving. However, it's likely we'll get good 80/20 solutions where you get most of the performance of the then-unsustainable high end models for significantly less compute.
>It can't 'learn' on its own.
Nearly there today. https://www.anthropic.com/institute/recursive-self-improveme...