01:45:39 · John Coogan, Thibault Sottiaux
[01:45:39] S1: Yeah. Can you talk about how the role of member of technical staff is evolving? Because you're you're you're talking about Soul Ultra going off and working for days at a time. And at a certain point, your job sort of evolves to if you have a launch tomorrow, don't kick off a task that takes four days. Even if the model's capable of it and will deliver something great in four days, you need it tomorrow. And so you have to size your workloads appropriately. How is the how how are you thinking about sizing work and and actually delegating the right the right chunk of work at this stage? [01:46:19] S4: Yeah. I find your question very interesting because, it actually highlights, like, a shift in our thinking over you know, since we had five sixes, you don't really instruct necessarily, you know, for a task that's gonna take four days. It's like, you know, you tell it all the information that you have. So, know, you're like, Hey, I have a launch tomorrow. Yeah. Right? And then it's like, keep track of the time and like understand that, you know, the PR needs to land, like, you know, I'm in that or like by 2AM, it will just like reason over it. Yep. You're not the one that needs to manage like, you know, all of that extraneous like complexity. Yeah. And so that's it. What you're seeing as well, like, you know, your relationship with the agent like changes over time as it gets more intelligent and you're just like, oh yeah, can just talk to you like, you know, another like, you know, super smart human.
01:46:19 · Thibault Sottiaux, Jordi Hays
[01:46:19] S4: Yeah. I find your question very interesting because, it actually highlights, like, a shift in our thinking over you know, since we had five sixes, you don't really instruct necessarily, you know, for a task that's gonna take four days. It's like, you know, you tell it all the information that you have. So, know, you're like, Hey, I have a launch tomorrow. Yeah. Right? And then it's like, keep track of the time and like understand that, you know, the PR needs to land, like, you know, I'm in that or like by 2AM, it will just like reason over it. Yep. You're not the one that needs to manage like, you know, all of that extraneous like complexity. Yeah. And so that's it. What you're seeing as well, like, you know, your relationship with the agent like changes over time as it gets more intelligent and you're just like, oh yeah, can just talk to you like, you know, another like, you know, super smart human. [01:46:58] S0: Yeah. Yeah. Talk about the efficiency of the model, what work went into that, why why it matters, you know, what kind of conversations you're having with, you know, big customers, all that stuff.
01:46:58 · Jordi Hays, Thibault Sottiaux
[01:46:58] S0: Yeah. Yeah. Talk about the efficiency of the model, what work went into that, why why it matters, you know, what kind of conversations you're having with, you know, big customers, all that stuff. [01:47:10] S4: Yeah. So what matters a lot right now is sitting at the frontier and you know, getting the max capability when you want it, but also for your normal average day to day task is like, you know, being super efficient and not just for latency, just because also we're seeing, you know, so like we had this era of token maxing and then, you know, we've been talking a lot with, you know, all the companies and enterprises that we're working super closely with and then they were like, oh, it's just a little bit, you know, maybe out of control. It's like, you know, what we want is like, you know, were in a highly efficient model that is steerable, controllable. We want the right, you know, spend control dashboards. And so we also have all of that. You can look at your spend, understand the ROI, but also you can rest knowing that, you know, this is actually like a super, super efficient model. And so it gets the job done with, you know, way way way fewer tokens, which you know, to you, you know, means that, know, you have to pay less for the same results, which is super important. And this is really the theme I think of the year is like, you know, that being on the efficiency of like, you know, performance Mhmm. And and and cost.
01:47:10 · Thibault Sottiaux, John Coogan
[01:47:10] S4: Yeah. So what matters a lot right now is sitting at the frontier and you know, getting the max capability when you want it, but also for your normal average day to day task is like, you know, being super efficient and not just for latency, just because also we're seeing, you know, so like we had this era of token maxing and then, you know, we've been talking a lot with, you know, all the companies and enterprises that we're working super closely with and then they were like, oh, it's just a little bit, you know, maybe out of control. It's like, you know, what we want is like, you know, were in a highly efficient model that is steerable, controllable. We want the right, you know, spend control dashboards. And so we also have all of that. You can look at your spend, understand the ROI, but also you can rest knowing that, you know, this is actually like a super, super efficient model. And so it gets the job done with, you know, way way way fewer tokens, which you know, to you, you know, means that, know, you have to pay less for the same results, which is super important. And this is really the theme I think of the year is like, you know, that being on the efficiency of like, you know, performance Mhmm. And and and cost. [01:48:11] S1: What about if you wanna spend more for faster, performance? What does the future of, either ASIC enabled, Cerebras enabled, Spark, and and fast mode, What do you wanna see develop there, either immediately or over the next couple weeks?
01:48:11 · John Coogan, Thibault Sottiaux, Bernt Øivind Børnich
[01:48:11] S1: What about if you wanna spend more for faster, performance? What does the future of, either ASIC enabled, Cerebras enabled, Spark, and and fast mode, What do you wanna see develop there, either immediately or over the next couple weeks? [01:48:27] S4: Yeah. I think what we are, truly working towards, like, it's a buffet of options. Right? So Mhmm. For for your normal, like, you know, interactive task is, you know, you're going to use five, six sole on medium or on high and you're going to have an amazing time. [01:48:41] S5: If you [01:48:41] S4: have a really hard problem and you're trying to, for example, find a cyber vulnerability in something, you're going to run ultra and you're going to run it for two days and it's just going leave no stone unturned and like, you know, invent novel techniques and you know, you're going to be like absolutely blown away by what it comes up with. But a lot of times you also just need speed, you know, like for some of the steps that we're dealing with is like, you know, we love working off of the Cerberus version of this, which is like at, you know, about like, you know, seven fifty tokens per second, which is an order of magnitude faster than the default version that we have on the API and in the product today. And this is just really situational or if you just want the very best usually absolutely no compromise. It does come at a cost. [01:49:27] S1: Of course. A lot of
01:49:27 · John Coogan, Jordi Hays, Thibault Sottiaux
[01:49:27] S1: Of course. A lot of [01:49:28] S0: people were feeling left out this week that weren't in the early access program. What makes a good early access partner? I'm sure your DMs are just making people that want access to the next set of models, but what makes a good partner to the to the to the product and the research team? [01:49:45] S4: Yeah. We really try to go as broad as possible. It is quite a bit of effort to manage, and then also, like, we're getting all that feedback and incorporating it, we work very closely with the the the folks in early access. For us, it's just really about realizing whether it is as good as we think it is, right, you know, it's like so close to the model, you train it, you know, you've incorporated like all that feedback, all your dreams, visions into this model, and then you've played with it for a little bit. And then when we give access to, you know, folks outside of OpenAI, it's like, you know, the first time where we have like an unbiased look, know, where people are using all sorts of models and different harnesses every day. So it's just kind of like this awesome, you know, is it actually as good as we think it is? It's like, know, what are the things that we missed? And so it's that you know, high high bandwidth engagement, good feedback, and then, you know, that sort of people who have shown to be unbiased in the past and, you know, talked honestly about, you know, all sorts of models and all sorts of harnesses.