Of course? On average, companies that are still standing after ~15 to 20 years are going to be a lot larger than the companies who started more recently.
they're a replacement for cargoships. If done right, they're faster than a cargoship, but could (maybe) carry lots of capacity for cheap, while also not requiring them to take the same routes as a ship.
This seems broadly good. If you told me a democratic admin had recruited these people, I would think "wow! what a positive signal for the current admin!"
The Obama admin created the US Digital Service — i.e. the unit that “DOGE” has now reappropriated — which attracted and hired folks like Google’s Matt Cutts [0]. The USDS did not, as far as we know, try to sneak in fresh-out-of-college-grads by bypassing the normal hiring process.
This only makes sense if you think scaling laws won't hold.
If someone gets something to work with 1k h100s that should have taken 100k h100s, that means the group with the 100k is about to have a much, much better model.
San Francisco Compute | Multiple Roles | San Francisco | ONSITE
We're building a new, regulated, commodity market for large scale GPU clusters. More like the Kalshi, less like craigslist. To accomplish this, we make a VM orchestrator called "Fog" that understands InfiniBand, GPUs, and heterogeneous hardware setups.
We make money by taking a flat fee of a few cents per GPU hour upon delivery. Up until recently, we've been "running a market" via a spreadsheet, by selling bursts on very large clusters (think 6 to 8 figures deal sizes).
SFC started because Alex (https://alexgajewski.org/) & I went to go train an audio model and none of the vendors at the time would sell us a month-long contract. So we bought a year-long one, and tried to sublease it at cost (https://news.ycombinator.com/item?id=36933603), so that way we could buy just one month.
The goal of the company is the same: we want to make it possible to buy a big, giant training cluster for a short time period. We think a liquid market will let you spend $40m for a month instead of $300m for a year. If we can't make that happen, then only the big labs will get to make AI.
Most of our code is in rust, some typescript. We're about ~20 people. We raised $12m a bit ago. There are 7 ex-founders on the team.
We just go down a lot. It's VERY beta at the moment; we literally take the whole thing down about once a week. So if we know of some major problem, or we're down, we just don't let people on (since they'll have a bad experience).
You're right though that the prices are probably lower because of this. That's why we have a thing on our website that says "*Prices are from the sfcompute private beta and don’t represent normal market conditions."
If you'd like on anyway, I can let you on, just email me at evan at sfcompute, but it may literally break!
If I may recommend: put a note where people will see those prices so that they understand those prices are unlikely to remain. If the outcome of your current UX is people thinking you're a scam, you have a problem that will last as you start to scale. It's hard to measure now, but it's harder to fix later.
Also, I'm really impressed at how great your replies about your product are! You're a gem.
> Tech giants and beyond are set to spend over $1tn on AI capex in coming years,
with so far little to show for it.
Regardless of whether or not the implicit claim here is true (the claim being "all this spend won't produce an ROI"), the explicit claim here is nonsensical.
Of course the $1tn in capex has nothing to show for it! The spend has not happened yet! Of the spend that _has_ happened, most of the chips are not physically in data centers yet. Of the chips that _are_ in data centers, most of the models are not yet trained!
And of the models that _have_ been trained, many have clearly had a significant ROI. GPT-4 cost $100m, and OpenAI's revenue is now reported to be $3.4 billion a year.
Saying there's "little to show for it" is an absurd claim; the products are _printing_ cash! We beat the turing test! You can drive around in a self-driving car!
It's perfectly reasonable to say "where does the ROI come from when you spend $1tn on capex", but it's hard to argue against the success of the spend of the last generation of models.
I guess? It's very clear that the Turing Test is not a sufficient benchmark for intelligence, though, because a lot of the answers I get (far too many, compared to a human) are nonsense, technically. They feature correct grammar, and they sound correct, but are very, very wrong. In fact, a lot of the responses I get are simply catastrophically wrong when I ask technical questions. I wonder what else these things are wrong about? There's no way it's merely technical stuff, where usually there are only a small number of correct answers, if there are multiple correct answers at all.
My employer has bots in Teams which have been trained on years of questions and answers and the answers they give are so incredibly wrong that we've replaced them all with fixed-response automation. GenAI has been a severe disappointment, even for me, and I had almost no hopes for it at all and have poo-pooed the technology from the start.
> You can drive around in a self-driving car!
Can you? Really? Or do you need to be there to prevent the car from doing stupid things? The only fully self-driving vehicles that I know of either follow fixed routes, operate in a small, fixed area, or do not carry humans at all. Another example of where many AI promises were made, and the results have simply failed to materialize.
Generative AI, at least for anything useful, has been a severe disappointment for me. Even if I spent $0.01 per year on it, I would consider it a waste of money.
> Whether (people) get insurance, or what the rate for their insurance is, or legal decisions or employment decisions, whether you get fired or hired, could be up to an AI algorithm
This is a bit like trying to regulate horseshoes while everyone else is talking about speed limits & seat belts. Both parties say the word "carriage" and "passenger", but they have completely different ideas in their heads about what is about to happen.
