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This leaves a lot of questions. Why are they raising the age limit? What legislation demands this? Will other services follow? And what if parents give consent? Are users on a family plan now forced to kick off their kids from the joined plan?

Drobpox has an AI backend running on their own clusters. IOW, Dropbox runs their own local models, integrated to their offerings. As their AI offerings get more features, I believe they want to be on the safe side, so they increased the age limit.

Looks like StarCraft meets Starship Troopers but without that extra pinch of irony which likens this trailer to a fascists wet dream.

Maybe true for frontier LLM, but there's plenty of space in the niches. For example, I think their TTS/STT models are pretty good, speaking from personal experience.


Is there money in niche models?


Is there money in frontier?


Yes...?

Is that not self evident by the insane revenue from frontier labs?


Revenue is not profit.

https://isaiprofitable.com/


AI is profitable. Gross margins are high.

The only reason OpenAI and Anthropic are making a loss is due to training new models to keep up with competition - not because the industry is flawed in terms of business model.


> The only reason OpenAI and Anthropic are making a loss is due to training new models to keep up with competition - not because the industry is flawed in terms of business model.

So in other words they are not profitable? Like you cant say they are profitable and then in the next sentence say they make a loss. That is not how profit works.


Keeping up with competition is part of trying to stay in the frontier. If they stop training the profit disappears in a few months.


The point is that the model is already immensely profitable. There is no fundamental reason why AI isn't profitable. It already is.

Insane competition does not last forever. When chip manufacturing first started, there were dozens of companies that had fabs that made compute chips. Nowadays, only TSMC is viable. Samsung and Intel survived because of geopolitics.


> The point is that the model is already immensely profitable. There is no fundamental reason why AI isn't profitable. It already is.

We'll see I suppose. Until the audited financial statements are released, no-one who doesn't work for an AI lab can be sure.


Lol that chart is hilarious when you look at Nvidia.

Always sell shovels in a gold rush I guess


I mean yeah


> Is that not self evident by the insane revenue from frontier labs?

No...? Of course not?

Because revenue is only one side of the equation. Did you ever look at total cumulative OPEX and CAPEX, and how long it will take them to even just break even at current growth?


Anthropic is growing 10x revenue every year.

They're likely over $80b ARR by now. They'll be at $800b ARR next year at the same rate. Let's say their growth gets cut down to 3x instead of 10x - that's still $240b ARR by this time next year.

When you are growing so fast, you don't need to make a net profit. You just need to make sure your unit economics are good - which it seems like they are given reports that their gross margins are at 60-70%.


> They're likely over $80b ARR by now. They'll be at $800b ARR next year at the same rate.

And they will be 800 trilion ARR in a couple of years, following that same rate! 8 quadrillion by 2029!

> When you are growing so fast, you don't need to make a net profit. You just need to make sure your unit economics are good - which it seems like they are given reports that their gross margins are at 60-70%.

If their margins were anywhere near this good, they wouldn't need to raise so much money so often.

If you create a machine that turns 1 dollar into 3 dollars, you don't dillute your ownership of the machine, you use your fabulous profits to expand your machine's capabilities.


  If their margins were anywhere near this good, they wouldn't need to raise so much money so often.
Why not? They are reinvesting into growth. There isn't a clear winner yet and Anthropic wants to make sure it is one of them. Taking a profit now while letting OpenAI take your marketshare and train better models is not very smart.


Or they bleed money like crazy, and their margins are pretty awful. Which is the correct answer.

Your $200 subscription is a major net loss for them. The vast majority that pays for that would cancel in a heartbeat the moment they had to pay API prices. Which may or may not be profitable, I am not entirely sure. But for the sake of argument, let's assume that it is.


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?


Are we still calculated $200 subscription token spend based on their highly inflated API token cost and then concluding that they must be losing money on all $200 subscriptions?


Are their API token costs highly inflated? I see no evidence of that.


Sure, there is revenue, and market valuation. Is there _profits_ in frontier models ?

What is the horizon for openai and anthropic to _make_ money ? Will they achieve that by charging more for frontier models, or investing slightly less in training frontier models, etc... ?


This mirrors my experience:

- Store session turns in an sqlite-vec

- Provide the agent with an mcp to search the vec-db

- Let the agent write notes in md files along with an index / frontmatter

Along with the commit history, the vec-db gives the agent long-term memory. The notes allow the user to correct accumulation of false lessons.

Simpler but better.


Deepseek and GLM answered correctly on Openrouter when using non-Chinese endpoints. I hope it stays that way!


correctly is very loaded here. correct according to whom? the truth is different though.


There‘s only one truth, the rest is interpretation. I was looking for the non-Chinese interpretation.


On some days I wor from home but need to be present and the sun is out. So I take my laptop outside and work from there. I cannot do this with a desktop. Wouldn‘t the workaholic just stay inside and miss out?


Sometimes I'll just pop into a meeting from my phone and walk around. That said , I do have a laptop but only because I go to the office every so often . Otherwise, I work exclusively from my home office because that's where my home desktop computer is and I control everything on my work computer from there.


Camera on or off?

I love taking meetings while walking but since Covid everyone has their camera on making sedentary meetings mandatory.


There's a lot of value in camera-on, but for some meetings, I love being camera off, nowhere near my computer, somewhere else in the house, watering plants or sweeping the floor. Sometimes it actually has me MORE engaged with the verbal aspect of the meeting, listening better, speaking better, because there's less distraction from Slack and Hacker News...


I'm much more attentive in some meetings when my hands aren't on the keyboard. For particularly important meetings I'll get out my knitting so that I can be close by while having something that's not failing unit tests occupying my hands


Off now, but historically even at camera on companies I'd do whatever and just hold my phone when convenient for me or just shove the meeting in my pocket if I need my hands.

Obviously depends on your team culture, but try it :)


Wireless headphones can get you halfway there if they have enough range for ya to walk around the house a bit. Airpods are quite good for this.


What if there was a way to use your home computer from a very cheap laptop?


I built a weather app for myself fetching ECMWF ensemble forecasts for my home location. Running the service is 60mb plus data 39mb and another 2mb for the spaghetti charts showing temperature, clouds, precip, wind over 10 days. What do I need to do to fill the other 900mb?


Run those things as an electron app? Add on analytics, location tracking and data exfil and you'll be fairly close


Getting from using Yandex to funding Putin‘s war requires quite a few turns. Since most of Russia’s war chest is filled by money from oil exports and China indirectly also supports Russia materially, I would first cut myself off of all oil-based products, and then from the Chinese supply chain of goods, and only then cut products indluding Yandex search results. In that order, because that matters for the war chest.

And after you then stopped typing the response, otherwise having to use a device that was made in China built from oil-based components using oil—based transport throughout its supply chain, send a postcard with your apology to the Kagi team and anyone else who does not fund wars and still uses traded goods, because this is how the world is.

If you want to throw a however tiny wrench into Putin‘s war efforts, complain load to your government to join sanctions and to have them support Ukraine and make your choice at the ballot box accordingly.


> send a postcard with your apology to the Kagi team

why? Kagi is not entitled to be paid by them, and on free market they are free to not buy a commercial product and use alternatives

and avoiding Kagi is trivial to achieve while avoiding products that were produced with oil in some way is impossible


Why? Because the claim is that they 'fund' the war by paying Yandex.


And it remains a true claim.


I am using Orion on Mac and iOS for a few years now and I cannot disagree more.

If a site works on Safari but not on Orion it is mostly due to ad blockers etc. Just flick the compatibility mode and it works. I have not encountered a single case where this did not fix it.

Also for a while now Apple Pay works, Apple password manager works, autofill works.


+1 - using Orion on iOS & MacOs for a few years and i like it!


Unless we do our own benchmarks, we have to take all the marketing fluff from the frontier labs at face value, and all public benchmarks degrade eventually as labs optimize towards them. OP’s approach is wasteful because it is brute force, but post says that an ELO is kept, so this is also an experiment, and I don‘t see what‘s wrong with that. You learn which model performs well in which settings which may save resources later. It‘s also wasteful to keep working with the wrong model/harness/tools for too long.


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