I'd imagine that OpenAI would try to stagger their announcements, rather than publishing them recently close to each other. Is this because their previous post (about navier stokes problem) was met with controversy?
I'd imagine that OpenAI would try to stagger their announcements, rather than publishing them recently close to each other. Is this because their previous post (about navier stokes problem) was met with controversy?
I genuinely think that AI has accelerated so many different things that announcements from all companies will be incredibly common and fast.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
Not only that, we're making so many tiny improvements and bug fixes that improve the experience but we don't even bother to make those announcements anymore. They don't feel "grand" enough anymore. The goal post has shifted a lot in the last 6 months.
I apologise if this sounds mean spirited, I find posts like these making grand claims without taking the time to present facts that back the magnitude of these claims simply add noise to the discussion.
You could've stopped with just the first sentence and I've would learned just as much as I did reading that comment to the end.
I've been tracking Github commits per week for my team. Here's how it's looking:
https://imgur.com/a/GvByYrD
Our own Github commits volume seem to follow quite closely with token usage on Open Router:
https://openrouter.ai/rankings
Do you also have a graph for more useful metrics, like number of requested features delivered? Commits, lines of code, headscratch count... there are a lot of metrics you can use, but LLMs are notorious for increasing code verbosity - which adds noise to the already imprecise metric you linked.
Let me ask you. What are you doing such that your velocity hasn't been greatly accelerated in the last 6 months? Can you prove that it hasn't been accelerated with facts?
My bad - I misread your original post as general company announcements, rather than feature announcements. Now my question is, if your internal staff are not requesting these features and are struggling to adapt fast enough, how useful are they? To your last question - what do you mean by velocity? Velocity is speed and direction. You may have speed, but beware the brownian motion that is stochastic predictive models, as that will give you net zero velocity. I have my goals and know what I am doing, and to be frank, it matters more that I read and understand my own code than race to a local optimum.
> I feel like we've made more feature announcements to our internal staff than they can handle.
And your solution to staff being unable to handle the number of feature announcements be like..?
> more feature announcements to our internal staff than they can handle
have you considered the consequences of that or are you still drunk and thinking that this is a good thing?
They solved the code, now they're gonna solve the people away.
[dead]
This is needlessly combative and insulting.
Which company?
This is just a blogpost, rather than a major announcement (FLT proof was closer to the latter than the former)
At the rate this field is accelerating maybe this IS staggering
They are desperately rushing to IPO before the bubble bursts.
AI just solved a millennium prize problem. In a matter of days. Because of a rumor that someone else solved the same problem with AI.
What exactly would AI have to do in order to not be called a bubble?
How these things are even connected?
The current prices the largest players set for their models are not profitable, they bleed money. Eventually they will "fix" it. It could end up making their services less affordable and it could cascade other businesses and services that are dependent on them go out of business
> The current prices the largest players set for their models are not profitable, they bleed money.
How are the open-weight Chinese models staying ~6-12 months behind on widely distributed / commodified hardware, and serving for even lower prices?
By distilling the US SOTA models, which is cheaper than creating from scratch.
You don’t understand what a bubble is. How good the technology is is irrelevant. That has nothing to do with an economical bubble. It’s all about massive capital misallocation driven by a frenzy of FOMO, which is specifically the case for AI investments. Economically speaking what is happening is the most obvious bubble possible, it follows everything that would be expected from a bubble where companies are chasing an ill-defined grandiose dream, based on a new technology we don’t understand and has very dubious ROI, selling some vague future utopia, allocating massive amount of capital to build infrastructure dedicated to a very early versions of that technology.
As things mature there will be a correction, ie the bubble will pop.
I would recommend to read « Boom and Bust: a global history of financial bubbles » https://pure.qub.ac.uk/en/publications/boom-and-bust-a-globa...
As long as the data centers are utilized and generating revenue, I see no reason for a correction or any misallocation of capital for the infrastructure buildout.
And today these data centers are fully utilized. OpenAI tweeted today that they may need to disable new signups for the Pro subscription in the near future due to capacity constraints.
A year from now, who knows what the situation is going to be like. It seems quite possible that robotics, self driving, research, etc. drive even more demand and revenue.
Stating with any certainty that allocating capital to build infrastructure is a mistake and that there is a correction coming seems unserious.
> A year from now, who knows what the situation is going to be like.
That cuts both ways, we are building datacenters for an immature technology that is quickly evolving. We have no idea what AI will look like in the next 5-10y. Everything that is planned to be built is based on the demand we see right now, not what it will be in the future. That means different GPUs that require different cooling systems, different power supplies, etc. NVIDIA already broke backward compatibility with their new cards, which requires a different infrastructure.
What is unserious is the opposite position: believing that we already know what will be valuable in the future and bet the entire economy on it, without any proof of positive ROI.
Directionally we are seeing demand for more compute.
It's a reasonable assumption that data centers that are set up for large power usage and cooling will be valuable.
Claiming the opposite based on, well, nothing at all, in order to forecast a correction, seems less reasonable.
"Compute" is not one generic commodity. Filling your datacentres with ASICs that do nothing but compute SHA256 for Bitcoin mining was a smart move in 2015 but today that hardware is worthless e-waste.
What does the depreciation curve look like for nvidia cards purchased today? How long will it take to recoup the investment on this buildout? Will those datacenters pay for themselves before they're scrapped?
It's an interesting question. Some napkin math:
Let's say we serve a Fable class model on 8x B300.
From Kimi K3 metrics, with 8x concurrent streams, we would achieve 55-60 tok/s per stream, matching Fable 5.1 throughput.
432 tok/s x 3600 => 1.555M output tokens/h x 50$/M API price = $77.76 revenue per hour.
Assuming total API billing at 2.06x output token bill = $160.2 / hour or ~$20 per B300.
A server with 8x B300 could be $461.5k.
At an obviously unrealistic 100% utilization we would look at 4 months of revenue to match the cost of the server.
About how model serving works at scale and actual utilization I know little.
And for all we know Anthropic could serve their model with 64 streams on the same hardware instead of the 8 we assumed here.
You should see all the misallocation that was put towards valve-based computing. Why didn't they just all arrive at the correct answer without investing in discovery first?
Its resource allocation problem, same happened with .com bubble, lots investors put tons of money into dark fibers. Were they useless? No its very useful.
If you want check a example company from the .com days check cisco, their stock peaked at 75 then crashed hard and only managed hit that again thanks for the AI bubble.
Per Anthropic's prospectus: generate $30T (~94% of 2026 US nominal GDP) in revenue.
If non AI companies, in particular non tech companies start making unprecedented amounts of profit, inflation adjusted I will concede.
That being said I think LLMs are impressive, still.
You really believe there will be less demand for AI in the future?
No, I think AI models will be cheap commodities available from hundreds of providers for a few dollars, like a Linux VM is today.