00:08:57 · Ruby Justice Thelot, Bernardo Aceituno
[00:08:57] S1: Yeah. What I'm noticing is that, of course, people care about open weight models. They say they care about privacy and token cost in theory. But then when it comes down to it, it's still really hard for a lot of these enterprises to actually integrate AI into their processes. And so they end up just going for whatever tools are easiest to use despite it may be being closed or open. [00:09:18] S0: Yeah. Yeah. And at the end of day, hyperscalers are playing a big role there and making this more accessible. In the neo clouds as well, the fact that together, yeah, IOLAN and whatnot are making it so easy for you to deploy an open wave model, also enables enterprises to work together. I work with a customer that is a top defense contractor, and these people cannot have things that run-in the Internet. And they have entire data centers of GPUs that run Mistral, that run Muse, that run GPT open source models for their use cases. And for them, it always felt like a big trade off, but they were always ready to, like, set up that infrastructure. We look at enterprises, they are used to spinning out open source infrastructure here and there. So it's true that for a long time it has been challenging, but infrastructure has gotten to a point where it's becoming very easy. And I wouldn't say that it is a challenge for them to like, really like deploy, so they have to go to the easiest way when they're available. I think it's a challenge for them to know when is it appropriate to deploy it when it's actually like measurably functional enough for them.
00:09:18 · Bernardo Aceituno, Ruby Justice Thelot
[00:09:18] S0: Yeah. Yeah. And at the end of day, hyperscalers are playing a big role there and making this more accessible. In the neo clouds as well, the fact that together, yeah, IOLAN and whatnot are making it so easy for you to deploy an open wave model, also enables enterprises to work together. I work with a customer that is a top defense contractor, and these people cannot have things that run-in the Internet. And they have entire data centers of GPUs that run Mistral, that run Muse, that run GPT open source models for their use cases. And for them, it always felt like a big trade off, but they were always ready to, like, set up that infrastructure. We look at enterprises, they are used to spinning out open source infrastructure here and there. So it's true that for a long time it has been challenging, but infrastructure has gotten to a point where it's becoming very easy. And I wouldn't say that it is a challenge for them to like, really like deploy, so they have to go to the easiest way when they're available. I think it's a challenge for them to know when is it appropriate to deploy it when it's actually like measurably functional enough for them. [00:10:18] S1: Definitely. Well, Bernardo, really wanted to thank you for joining us today You on know a lot about open source. You know a lot about enterprises at your role at Stack AI at Asana. So really wanted to thank you for joining us and sharing all of the takes. [00:10:31] S0: Think you're always. Thank you.
00:10:33 · S2
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