It really isn't. We get coding work actually done today on Opus 4.5. That's not SOTA any more, and anything proximate to that level, even quite loosely, is genuinely useful.

> "can I get my coding work actually done today" vs. "this can do customer support chat"

I think you need to define "can get coding work done" for this to make sense. Ive been using GPT-3 back-then for basic scripts, does that count ? Or only Claude-Code ?

I also think this is a false dichotomy, if you look at the Project Vend project or Vending-Bench, customer support etc. is at no means trivial. (Old but great story https://www.businessinsider.com/car-dealership-chevrolet-cha...)

SSO Tax is a large part of it, controls around plug-in marketplace, enforcement of config, observeability of spend. But it’s all pretty weak really for $20 a month.

And Microsoft are going the same route to moving Copilot Cowork over to a utilisation based billing model which is very unusual for their per seat products (I’m actually not sure I can ever remember that happening).

> But how is that a counterpoint to tokens being subsidized? They obviously are subsidized, this just isn't arguable at all.

Why would Microsoft subsidize Anthropic's models when they serve the Claude model on Azure? They charge the same price as Anthropic. They aren't an investor in Anthropic.

There are numerous independent model serving companies that are clearly profitable serving non-Frontier models (Kimi K2.5 etc). It's easy to work out the raw costs of B200 GPUs, and then see what you need to charge for an API and see they make money.

The frontier labs charge a lot more than these companies.

The frontier labs have said they are profitable on inference.

Most people believe that training (and maybe subscriptions for some users) is where they lose money. Why do you think otherwise?

Hi Mike! Long time - super nice to see your name in my HN feed.

I’ll fight you on profit. The major labs are super profitable. If you replace “profitable today” with “cashflow positive today” then I think you’re correct. They are clearly not cashflow positive today. However, they are absolutely profitable, and when people confuse those I think it can be dangerous.

Consider a series of companies, let’s call these companies “Claude 1, Inc”, “Claude 2, Inc”, “Claude 3, Inc”, “Claude 4, Inc”.

In each company let’s keep track of the following:

* The pro-rata hardware and energy costs the company used during training. So, for instance, if a cluster is going to “last” 5 years, and we used it for 2, and the cluster cost $1 billion to build and provision and pay for 5 years of energy usage, we would charge $200mm.

* The R&D expenses like salary and so on

* The inference costs of every use of that company’s model.

* The revenue acquired in exchange for use of that model.

I propose first that I haven’t hidden any costs or double counted any revenue or anything - this is a full, fair assessment of the costs and likewise the revenue earned. I propose second that if you go to the end of the company’s final period then “profitability” in this case equals “cashflow”, so we can talk about either without talking past each-other. Third, I propose - if you add up all the costs and expenses of Claude 1 - 4, Inc, you’d have the full P&L of Anthropic, up to any training done on Claude 5.

I will now repeat a statement made publicly and repeatedly by Dario (and Sam in a slightly more cagey way): every single one of those “companies” (fully loaded models) has turned a profit so far. Put another way, it has, repeatedly, been a very good financial decision to train a model, and then sell inference of that model.

Why are the frontier companies spending cash? Simple - as each new model comes out, it’s quickly apparent that the new model will pay, and so increased training costs are incurred before that model has ended its useful life. Due to scaling activity, each new run costs some multiple of the prior run. Combining the overlap and the scale up, these companies are cashflow negative. But they aren’t doing it in some weird race to spend a dollar to make $0.50. They’re spending a dollar to make like $6 a year for a year or two.

If you see this, most of the ‘bubble’ (and implied massive crash) forecasts don’t seem to have any basis in reality from my perspective.

Frontier lab models are fucking great earners: 60%+ inference margins. (Public statements by said CEOs. Lateral proof: similar sized open models available for inference at 1/8 to 1/10 price on openrouter. Ergo - closed model margins are high). These earnings are real dollars, hard cash. Maybe the datacenters are in a bubble? After all, there’s a lot of debt getting laid on to do datacenter buildouts.

Datacenter companies and hyperscalars are making money providing hosting to these frontier labs. Coreweave (former ETH miner!) and others are posting 70% profit margins against debt costs under 8%. These profits are again in hard dollars from the labs. So, maybe the hardware providers are in a bubble?

Nvidia is making 70%+ margins, consistently beating every earnings call, is spending like $6bn a quarter on R&D against $40+bn in share buybacks (made in cash). They are moving super fast, and they could still literally be spending another 7x their current R&D spend before going cashflow negative. So, maybe the foundries are in a bubble?

TSMC is showing 66% margins (record high), and cutting Apple’s allocation to a point where there are research warnings about it. Maybe the EUV lithography companies are in a bubble?

ASML is the most generous company in the world, and is showing 34% operating margin this year while providing the only machines that can make the chips that TSMC and others are selling.

This is all very real. To my eyes the possible negative financial outcomes that seem plausible are:

1 - scaling laws stop working (and/or models get ‘good enough’) and all of a sudden the new hotness we just spend our entire last 5 years revenue on isn’t any better.

2 - There’s some major exogenous shift in demand for tokens and datacenter utilization drops radically, leading to credit defaults.

The main things that would have to be true would be that these things would have to be industry wide before they were a problem, and they’d have to end up with demand at less than 1/6 or so of current forecasts before they caused some kind of cascading financial problem: until then we’d see coreweave breaking even, reworking its debt covenants, spending less on power (unused), spending less on power (over capacity = lower prices on power being used), etc. etc.

This is SUPER long already, but to close, I think it’s reasonable and interesting to talk about those scenarios - how likely is it that scaling stops working or that people are okay with what we’ve got (that is, token value stops increasing in a compute-unitized environment)? How likely is it that people stop buying tokens at all even if their utility is stable or growing?

Agreed we’re in a temporary transitional phase right now, but I think it’s to a radically new business model and economic order more than it is a prelude to a giant debt leveraged crash, Wile E. Coyote style.