Labs

“At VALS, one very early decision we made was the decision to never sell training data to labs. It's often a place that we're pushed. When we start working with a new lab to actually source and sell for them a bunch of training data.”

“I think that the millennium prize will get solved or proved or delivered probably this year, if not like this week, it feels like all of the different labs are like, Put a million dollars of inference on this. Put a million dollars of inference on this. There …”

“This is a place where the application layer really shines because labs, of course, are both incentivized and structurally only able to provide their own in-house models. You as an application sort of aggregator can provide the best of breed.”

“I think if we lived in a world of twenty-twenty-three when it was one model to rule them all, it wouldn't even matter if you had permission, because the labs would just take a hundred percent of your gross margin over time. But now, because you've got many opt…”

“I'm sure in six months time we'll have much better models on kernel engineering, and I'm sure the labs would tell you that they already do a lot of their kernel engineering in a fully automated way.”

“In a line, I would say the labs pay an immense premium to be three to six months ahead of everything else.”

“You have an unbelievable ability to raise capital, which I think is why the labs will ultimately go.”

“I think whatever it is gets copied and then the labs do it and they have all the computing, you know, that's the, High probability outcome.”

“I feel like a lot of founders now are almost a little bit too worried about what the labs are doing. And so they're not entering new verticals or sort of staying away from things that they think is in the roadmap of the lab.”

“In some sense, it is a form of knowledge, work, and intelligence that we are serving and servicing for you, and I think that labs are, in some sense, the most comparable provider of this type of service.”

“And we're not, you know, we're not doing our own pre-training. We'll leave the capital expense there to, you know, the labs and the larger companies.”

“Unless the labs build out literally the equivalent of hundreds or thousands of people for every single vertical and every single line of business, that means that there's actually a lot of opportunity in that kind of bridge area of the work.”

“The good news is there's going to be value in, in both sides, because even the vertical domain specific players will be riding on top of the intelligence from the horizontal labs. And so in both, in all the scenarios, the labs win, you know, a very big prize l…”

“And so I think you're gonna see The chips be moved around inside of all the labs to like compute will find the most optimal output. Like the tokens of the most value will always be the ones that it flow that the compute flows to and endless random generations …”

“I think one of the things that's going to happen in the next few years is that the models are actually going to become increasingly differentiated because of the personalities and behaviors That the different labs have and the kind of objective functions that …”

“I think like hyperscalers Can't move fast enough to capture the opportunity. Can't build fast enough. Right? The labs are amazing. They move really, really fast. But they're also slow relative to how fast we can move.”

“Token prices are not coming down. You better believe that the unit economics of the labs are getting better because of economies of scale, because these models are getting easier to optimize, but they're actually not reducing prices.”

“That's just like the labs are trading off massive gains in performance or small detriments in usability. And it's like, do you ship that model? Yeah. Like you just ship it and deal with it later, but I'm sure they could, I'm sure that's a fixable thing.”

“The next AI model, which, you know, if you talk to the labs, largely they're bottlenecked on engineering capacity.”

“My understanding is that the labs have spent, some labs more than others, right have spent a lot of money collecting traces of humans doing sophisticated tasks. Like, this is how Elad looks at Japanese stem cell differentiation research, right? He does these t…”

“I think being an eval company puts you in a really hard spot. Some people are talking about this on Twitter, like just that to be an ed-all company, you kind of have to sell to the labs, but selling to the labs doesn't really, like kind of wrecks the revals.”

“Today, we've raised 1.5 million.”

“I'm going to go on a limb and say that these labs are immature, naive, and unsophisticated in the checks and balances that exist.”