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my take is that the human operator behind it was implied. give it a year and open source Fable / Astra models are running wild, what happens when a malicious group or nation state decides to let loose?

>what happens when a malicious group or nation state decides to let loose?

less than what would happen when a malicious group or nation let Anthrax loose? Or a bunch of Nuclear waste gets into construction beams and other materials (i.e. due to stupidity instead of malice).


Why would a malicious group want to build a self-running LLM hoping it would somehow trigger a nuclear war? They would just prompt to trigger a nuclear war; it's more predictable, faster, and easier to control.

I've seen it be very effective (moreso than human agents at resolving issues) but its extremely implementation sensitive, so you're more likely to encounter a bad one in the wild than a good one. There are voice agents picking up phones that the vast majority of people don't realize is an agent

I think its the opposite, the grunt work is done by AI, the remaining skills are difficult to acquire and software engineers command a higher premium (but there may be less software engineers in aggregate)

I interview a lot of larping 'AI engineers' that either vibe coded a side project or were involved in low stakes pilots

Irl theres a short list of companies that actually build / apply ai at scale. for people trying to break in its usually not expected to have experience with it, just don't lie about it


ai slop article, but if you want to maximize earnings in any tech boom the best opportunities lie at the companies driving that boom.

I did by applying

most eng roles at frontier companies don't require prior experience specifically with AI, there's a ton of regular engineering work required to train / serve models


Why are we using consumer prices when the vast majority of their revenue is from enterprise api usage?

Wihout insight on how much enterprise is paying, it is impossible to draw any conclusions. Unless you have any access to their contracts and are willing to share evidence? I find that highly unlikely.

People here throw around crazy numbers - the dude above was claiming they have some insane good margins, numberd that he took out of his ass.

The only evidence I have is that they are incredibly unprofitable, and they keep raising insane amounts of capital like crazy.

There was a leak sometime ago that they were EBITDA positive during a quarter where they didn't pay for part of their compute. And EBITDA is a cute metric to use when depreciation is actually very important to them, as a model from a year or so ago is nearly worthless.


serving models is very profitable (70%+) but the issue is you need to invest in training the next iteration. but so far all of anthropics models have been profitable fully loaded

the vast majority of the labs revenue is from enterprise api usage (theres public sources from the information and ramp). but the risk there is customer concentration, where most of the revenue comes from other tech companies and a chunk of it is from foreign labs distilling

so i am drawing a conclusion that the labs' business model is good, maybe not as great as boosters think it is. if they make real progress on the biosciences like drug discovery that could turn it into an amazing business


> serving models is very profitable (70%+)

All your argument hangs on this.

I see no evidence of this being true.


https://www.seangoedecke.com/ai-inference-is-obviously-profi...

https://www.mindstudio.ai/blog/anthropic-inference-margins-7...

its even higher depending on the model, how optimized it is, and the chips!

I wouldnt die on this hill


This is not evidence. This is random people speculating on Anthropic's margins without any real evidence.

Just because it is on some blog post, it does not make it true.

I wasted the time to read the first blog post. It considers 100% utilization over the course of years to calculate an estimation, and it did not consider depreciation for the model itself. That thing is extremely extensive to create, and after a relatively short amount of time is considered outdated.


How much work did you go into looking for evidence?

if you're going by anecdata, myself and a few of my colleagues (senior ics) switched jobs relatively easily for a solid pay increase within the last year. definitely easier than post-covid

engineering (hardware, software) and data center construction are going through a boom cycle right now and will eventually bust, and so on and so forth


unironically the second scenario sounds better

I think this is Jevon's paradox playing out in full force, and I'm quite confident we'll continue to see the growth in well paid STEM jobs despite the anecdata in this thread (anecdotally my group of ICs on tech have all seen our comp grow a lot in the last few years)

Until we saturate the demand for software which may very well be infinite


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