AI labs

“Because companies need to make money and governments want to have a productive economy, no one is working on AI, just doing whatever it wants to do because that doesn't make any money. So no, one's working on AI setting its own goals.”

“My, if I had to bet, my bet is that the enormous amount of capital and talent That has been aggregated, you know, around this relatively small number of companies because of the hype will be enough to unlock the next S-curve, and so you'll see, you know, kind …”

“That's why these AI application companies are going to win. That's why they're going to become so big, because I don't think at least in the next five, 10 years that any of the AI labs are going to build all the domain specific stuff.”

“It looks exactly the same as placing someone to work at any company, right? So just like Mercore might work with startups making their first hires or companies hiring in a more traditional full-time capacity. It's the exact same thing for a lot of the large AI…”

“Building applications on top of these models becomes, it is a lot easier, and you can go from zero to one, and you can be more nimble than even these labs that are gonna all have, like, you know, tens or hundreds of millions of users, and you have to move slow…”

“I think the more you are just, like, maybe it's good inverting that all to see, like, what the failure mode looks like. I think it is resting on your laurels or not retaining your best people. Just believing that making the models incrementally better in every…”

“More labs either choose to not show or otherwise obscure the chain of thought down the line.”

“Even, like, let's take within the labs, like, I assume every single one of the labs is using, like, even within themselves, like, it is very valuable to be able to take, you know, the knowledge of your highest-end model and then be able to make it higher, you …”

“We've definitely found those stories of customers where I think they start out multi-vendoring, working with a bunch of different customers, with different vendors, but ultimately get to the point where they realize that they're going to be making a trade-off …”

“I think there'll be a few winners. I think there'll be a few winners. A few because I do think for the labs, it helps them to have a few partners for resiliency. And I imagine also for price competitiveness. I think there'll be a few winners.”

“Those labs that have Capability to invent new algorithmic ideas are going to start having bigger advantage over the next few years. As the last set of ideas, all the juices being wrung out of them.”

“And now they're focused on how do we get the model to do this end-to-end workflow, coordinating with multiple colleagues for a financial model or a slide deck like we were discussing. How do we get the model to build an entire SaaS application end-to-end? And …”

“We are finding that the labs tend to prefer partnering with a very horizontally capable vendor that is able to flex across all of the different Verticals and scale extremely quickly rather than working with a hundred different vendors that they have to train f…”

“One of the largest things that people underestimate both in the context of AI labs as well as within the enterprise is how significant of a job category it is going to be to train agents.”

“There are like just labs for the sake of being lab, and I don't think they're going to make it.”

“This is just, like, VC-subsidized work that labs love. The problem is scaling it beyond a few data points or what one founder or full-time employees can do, and this is the position that we're in, is we're having to compete against Basically founder-led annota…”

“I do think Neo Labs, without a clear vision for how they're going to impact the world, I do genuinely think there is some risk that they will turn out to be interesting research project, but not a viable company.”