Tanjreeve

You see how the history of small print to everything they release to the media creates the accusations of running on misleading press releases?

Absolutely. And those limits + changes to them piss off a lot of users. There's a rock of they need to show adoption + usage while having to put limits on everything that pisses off users.

And there's a hard place that everyone freaks out at API prices and on-demand Opex models outside of a niche (I use API for personal research for quite focused queries and slightly more reassurance on being the customer and not the product) but this is very competitive + not sticky.

How much of their compute is leased/rented? Last I read it was quite a lot of it.

This metaphor gets repeated a lot for some reason but using a gym subscription has an upper bound. If one subscription could be using 10000 different machines at the same time their model would be entirely different.

API is definitely holding things up. But they excluded the training from those profit calculations. There's a ton of wordplay indulged online about how that's capex etc etc. But without the training they don't have the thing to run inference on so it's very fitting with the pattern of being evasive with finances.

This seems kind of aggrandizing when BigAI shifts goalposts constantly whether it's "we created AGI" by a definition that an autopilot in a commercial plane fulfills or "Maths research" where everyone else has to do the actual research.

I'm curious if it actually needs to be across AZs versus if it just needs a few instances but yes as written there is a trade off happening.

The other route for fast S3 writes others have used (Turso springs to mind) is express S3 zones but those have a lot of region specific fine print still.

In OP

> It’s not as simple as writing data straight to S3. Lots of PUTs for tiny blobs of data are slow and expensive. Instead, we use an SSD tier to optimize speed and cost:

> When you write data to an actor’s SQLite database, the write is acknowledged once SSDs in 2 of 3 availability zones have it. Then, the data is batched to S3 in the background. This means that S3 is never on the write path, which lowers latency and cost. When reading data or waking an actor, Rivet reads from SSDs if already cached and falls back to S3 if not. SSD caches can be tuned to meet your needs, giving you configurable tiered storage for your actors.

What a bizarre non-sequitir. Rent controls don't work so "the voters are voting wrong"

Someone ought to tell the voters who supported all the anti data center propositions and politicians that they're wrong and just imagining it not being handled well.

In seriousness this is an example of the previous point. Rather than engaging with the issue the tech/DC industry seems to think they can wordplay their way out of it by quibbling about irrelevant technical details and people are primed to get extremely angry at grinfucking type behaviour nowadays.

Why does "AI" matter in this? Pretty sure there are standards for dishonest or fraudulent in normal advertising.

Leaving aside that Meta are selling compute. The reason I pick on Google + Meta is they are able to make their own customers + charge their own prices. Anthropic + OpenAI are both reliant on people coming up with Products powered by their AI models valuable enough to create several AWS worths of revenue and also not also not immediately moving it to open models.

with the caveat being that I came into the industry in 2014 so I may have missed the golden age of the DBA fully - I worked for a DBMS vendor on sales/solutions side for a while and most DBA’s I spoke to were slightly more knowledgeable business analysts. I met very few who really understood IO and Paging and Locks and how they’d impact a DB’s performance. Which kind of makes sense because if you were really obsessively into that you’d probably do something different or you’d work for a business dealing in storage systems. The value a DBA has been very low in my career outside of forcing process improvement just by saying no to things.

Genuinely think google + Meta might be the only companies to make a profitable AI product at this rate.

> At this rate Anthropic will exceed AWS Revenue within a year

Well that’s the point. They’re not continuing at that rate of growth.

That seems like it goes without saying that yes the rules would need to change for results to change. Its just not a hard law or even some sort of natural state of humans.

You are absolutely entitled to call it a meme but that is also their own internal reporting/panicked emails. Subscriptions are offered at a large subsidy with the benefit of more training data and adoption.

> I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.

The big labs can't do this that's their edge over open models. The stickiness is critical for them to keep up. They might try again but it's going to finish badly.

"Latest frontier models" seems a bit like acting like smarter reasoning can fill in context that doesn't exist in various systems without guessing. It's convenient because it's always the users fault for not working in the platonic ideal observability setup that can feed everything on a plate. And if you're in that situation how much is the agent adding over incremental improvements?

It can't be both "so easy and hands free anyone can use it" and also "well they must be using the wrong model/holding it wrong if it's not working for them". If there weren't tradeoffs of the different models then it wouldn't be exposed to the user.

Was the main focus of your job genuinely using the aws CLI and pulling logs though? I was pretty good at it and jq but the only advantage I ever got from that was more time to deal with the problem on the ticket which has been 50/50 in my experience unless the ground is prepared with context + dev environment etc.

If people paid a profitable price for Opus 5.5 it would not be the best product in the world and would not be slotted into so many things where it's benefiting from ubiquity. That circle needs squaring. There will be an Opus 5.5 that's a 10th of the price in a few months.

Well they just need to use it properly and build a bunch of software systems for them to lever..oh hang on a minute...

The consumer product is losing them money and the possibly profitable enterprise product (API) is a smaller % of revenue and in an increasingly competitive niche. The constant disingenuous baiting and switching between the two whenever convenient is very tiresome.

If they can't raise capital then yes that will have a huge impact on a company that's a massive tower of financial engineering as coreweave have been learning.

As the conversation was about the societal trend. For the vast majority of people the answer is <= 1 month and it drops off precipitously adding months on.

Having something dumb that just cycles through the state of the art for exploits is already absolutely incredible security tooling (if people bother to make it into a process which I'm less optimistic about)

Odd behaviour that's unexplained creates conspiracy theories. That's a self created problem here.

That’s 22 million of capacity foregone for a marketing stunt. Or they had more capacity than demand which seems odd for a product that’s allegedly in constant demand etc.

There just isn’t a sane way to look at this as “investment”

Exorbitant Cloud costs and spending 22 million to solve a 1 million dollar maths problem isn’t investment though.

I think there’s some crossed wires here with the wider data centre build outs. OpenAI have their own flavours of sketchy behaviour but I’m not aware of them solely being on the hook for those amounts short of some weird accounting of Microsoft cloud costs onto them or something.

That’s mostly not the Frontier Labs. They’re definitely creating a lot of the demand but the people taking out those loans are Neoclouds and Big Tech (hence why Facebook + Google et al suddenly all have terrible cash flows after years of having more money than can be spent).

AnthropenAI are the ones driving a lot of that infrastructure spending when they’re such big consumers of compute but they’re not the ones feeling the brunt of the infra spending. Although those costs will bubble up to them indirectly eventually.