Anthropic raises $13B Series F

>>>> different commenter above: It is all fake and made up, and the numbers are detached from the real world, but it's not like the market doesn't know that.

>>> me: Perhaps there are salient differences between art on a wall and a company.

>> you: At heart, not really. The whole point of all of this is to motivate humans to get off their butt and reduce entropy.

> me: A painting on a wall is merely an inanimate object. / A company has agency; it seeks to add economic value to itself over time including changing people’s perceptions.

The Horror! Just look at the disjointed conversational history above. It seems like some sort of drunken history episode where people aren’t paying attention to each other.

Should I assume you are trying to understand what I’m saying? It is becoming less plausible with every comment. (I’m referring to the “be charitable” part of HN guidelines.)

Additionally, there is another anti-pattern at work here: this seems like a pretty inane definitional argument. You’re claiming there’s no difference between art on a wall and a corporation entity? By what definition? What is the utility of your definition; meaning, what can you do with your definition that provides differential predictive power?

My claim: when it comes to valuation, an agent is sufficiently different from a non-agent (yes, even if it appreciates!) What is the criteria for “sufficiently different”? To explain: if you get more benefit out of a distinction than it costs you to make the distinction, it is a net benefit.

In this case about valuing things, someone who makes a living building predictive valuation models is going to distinguish wall art from corporate entities because doing so is useful for prediction.

Of course they have some things in common. This is irrelevant to the question of “is making this distinction worth it?” As long as predicting the difference between them is valuable paying attention to the distinction is valuable.

This kind of talking past each other is one of many reasons “why we can’t have nice things” such as useful discussion. Shameful.

If you propose some grand unified theory that says two things ultimately derive from the same thing, that’s fine, but if you’re going to use it for prediction you’ll have to explain how to apply it.

Just so you know you are arguing with LLM output, not a human.

I'm not the one building out datacenters but I believe the power consumption is the reason for the devaluation. It's the same reasons we saw bitcoin miners throw all their ASICs in the bin every 6 months. At some point it becomes cheaper to buy new hardware than to keep running the old inefficient chips, when the power savings of new chips exceed the purchase price of the new hardware.

These AI data centers are chewing up unimaginable amounts of power, so if nvidia releases a new chip that does the same work in half the power consumption. That whole datacenter of GPUs is massively devalued.

The whole AI industry is looking like there won't be a first movers advantage, and if anything there will be a late mover advantage when you can buy the better chips and skip burning money on the old generations.

You can do some, but many of them have license restrictions that prevent you from using them in certain ways. I can buy an Intel chip and deliberately use it to do things that hurt Intel's business (e.g., start a competing company). The big AI companies are trying very hard to make that kind of thing impossible by imposing constraints on the allowed uses of their models.

> Just your GPU not counting the rest of the system costs 4 years of subscription

With my existing setup for non-coding tasks (GPU is a 3060 12GB which I bought prior to wanting local LLM inference, but use it now for that purpose anyway) the GPU alone was a once-off ~$350 cost (https://www.newegg.com/gigabyte-windforce-oc-gv-n3060wf2oc-1...).

It gives me literally unlimited requests, not pseudo-unlimited as I get from ChatGPT, Claude and Gemini.

> and with the sub you get the new models where your existing hardware will likely not be able to run it at all.

I'm not sure about that. Why wouldn't the new LLM models run on a 4yo GPU? Wasn't a primary selling point of the newer models being "They use less computation for inference"?

Now, of course there are limitations, but for non-coding usage (of which there is a lot) this cheap setup appears to be fine.

> It's closer to $3k to build a machine that you can reasonable use which is 12 whole years of subscription. It's not hard to see why no one is doing it.

But there are people doing it. Lots, actually, and not just for research purposes. With the costs apparently still falling, with each passing month it gets more viable to self-host, not less.

The calculus looks even better when you have a small group (say 3 - 5 developers) needing inference for an agent; then you can get a 5060ti with 16GB RAM for slightly over $1000. The limited RAM means it won't perform as well, but at that performance the agent will still capable of writing 90% of boilerplate, making edits, etc.

These companies (Anthropic, OpenAI, etc) are at the bottom of the value chain, because they are selling tokens, not solutions. When you can generate your own tokens continuously 24x7, does it matter if you generate at half the speed?