> At some point they're going to drop the real prices in your lab

The real price for tokens at a specific level of intelligence is dropping rapidly. Between models improving and new hardware getting faster, I think prices will be going down, not up.

> Better to get a handle on it now.

I experiment with a lot of providers and models. I strongly disagree that anyone needs to start migrating now in preparation for prices maybe going up in the future.

It doesn’t make sense. Deal with a price increase when it happens, if it does.

Switching models and providers isn’t some huge lift. I can jump to a new model or even hardness and get a feel for it in a day or two. It’s not some huge undertaking that requires preparation and planning.

If prices go up, try something at that point in time. Don’t worry about it until then.

It's not obvious the subsidization will end. Loss leaders are a thing. Most enterprise at this point are paying API pricing

To better predict what they'll do with it we'd need to know the proportion of token usage via API vs subscription. Keeping an affordable, slightly unprofitable subscription to get people in isn't a bad idea. They also get marketing benefits from the word of mouth

>At some point they're going to drop the real prices in your lab and you're going to have to figure out how to pay that much or pay someone else a lot less. Better to get a handle on it now.

So just wait until that happens? What's the point of scrimping and saving now? Not to mention you're paying for a premium in the meantime.

> At some point they're going to drop the real prices in your lab and you're going to have to figure out how to pay that much or pay someone else a lot less.

This only applies under the assumption that everyone tokenmaxxes their subscription.

I have visibility across a few different Claude team subscriptions. I'd say roughly 10% of people max their subscriptions.

I think I've only seen one organization where the under-used subs don't more than pay for the tokenmaxxed ones.

And that is ignoring the 90% margin that API inference runs at.

(meta commentary) really gnarly word choice: "drop" in "they're going to drop the real prices in your lab"?

I took your meaning as "they're gonna drop [a bad surprise on you by raising] the real prices"... so at least it's not quite as tortured as the ambiguous "support for version N dropped today" (where you often can't even infer the direction from context).

this isn't just a tangent, I'm having trouble understanding the point you're trying to make, which hinges entirely on deciding if you meant "drop" literally

Counter point - models get better, ways to run them gets optimized and hardware will eventually come down in price.

The share of tasks where "cheaper" models are enough also increases for same reason. For majority of use cases it will quickly become race to the bottom. And that means part of the pie for the "premium" models of frontier AI labs will quickly become smaller and smaller.

At some point companies will need to have the “scary” conversation about the value proposition of adding another human to the team, or adding a comparable amount of token spend to the team.

$1k/person sounds like a lot, but a ten person team at $1k token burn / head / month is spending $120k. How much does a new hire cost, tco? Does velocity from one more person compare with velocity from one more person’s worth of tokens?

Sure, learning to be efficient matters. So does taking the time to consider cost vs alternative. You might be surprised by what you find, in either direction.