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<v Speaker 1>Thank you so much, Lon for being here at build.

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<v Speaker 1>I know you started off as an intern at Microsoft,

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<v Speaker 1>you were a Windows developer, and of course you're a

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<v Speaker 1>big PC gamer. Still, you want to just talk about

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<v Speaker 1>you in your early days with Windows and the kinds

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<v Speaker 1>of things you build.

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<v Speaker 2>Yeah, well, actually started before Windows with us. I had

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<v Speaker 2>one of the early IBM PCs with MS DOS and

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<v Speaker 2>I think I had like a one hundred and twenty eight

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<v Speaker 2>K in the beginning, and then at double T two

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<v Speaker 2>fifty six K, which felt like a liar, so I yeah,

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<v Speaker 2>pro programmed video games indus and then in Windows remember

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<v Speaker 2>Windows three point one.

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<v Speaker 1>Yeah, no, it's wonderful. I mean even at the last

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<v Speaker 1>time I chatted with you, you were talking all about everything,

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<v Speaker 1>the intricacies of active directory, and so it's fantastic to

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<v Speaker 1>have you at our developer conference. Obviously, the exciting thing

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<v Speaker 1>for us is to be able to launch grock on Azure.

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<v Speaker 1>I know you have a deep vision for what AI

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<v Speaker 1>needs to be, and that's what got you to get

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<v Speaker 1>this billed. It's a family of models that are both

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<v Speaker 1>response and reasoning models, and you have a very exciting roadmap.

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<v Speaker 1>You want to just tell us a little bit about

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<v Speaker 1>sort of your vision the capability you're pushing on both

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<v Speaker 1>capability and efficiency, So maybe you can just talk about

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<v Speaker 1>a little bit of that.

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<v Speaker 2>Sure. So yeah, with with GROC, especially with Rock three

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<v Speaker 2>point five that is about to be released, it's it's

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<v Speaker 2>trying to reason from first principles, so apply kind of

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<v Speaker 2>the the tools of physics to thinking. So if you're

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<v Speaker 2>trying to get to fundamental truths, you you try you

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<v Speaker 2>boil things down to the axiomatic elements that are most

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<v Speaker 2>likely to be correct, and then you reason up from there,

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<v Speaker 2>and then you can test your conclusions against those axiomatic elements.

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<v Speaker 2>And you know, in physics, if if you violate conservation

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<v Speaker 2>of energy or momentum, then you're either going to get

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<v Speaker 2>a Nobel price or you're you're wrong, and you're certainly

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<v Speaker 2>wrong basically. So so the the that's really the focus

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<v Speaker 2>of Rock three point five is uh, sort of I

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<v Speaker 2>find a fundamental physics and applying physics tools across all

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<v Speaker 2>lines of reasoning and to aspire to truth with minimal error.

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<v Speaker 2>Like there's always gonna be some mistakes that are made,

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<v Speaker 2>but we aim to get to truth with acknowledged error,

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<v Speaker 2>but minimize that error over time, and I think that's

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<v Speaker 2>actually extremely important for AI safety. So I've put a

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<v Speaker 2>lot for a long time about AI safety and wealth.

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<v Speaker 2>Book conclusion is the old maximum that honesty is the

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<v Speaker 2>best policy. It really really is for safety. But any

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<v Speaker 2>want to have size. You know, we we haven't will

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<v Speaker 2>make mistakes, but we aspire to correct them very quickly,

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<v Speaker 2>and we are very much looking forward to feedback from

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<v Speaker 2>the developer community to say like, what do you need?

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<v Speaker 2>Where are we wrong? Cocker, we make it better, and

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<v Speaker 2>to have Rock be something that the developer community community

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<v Speaker 2>is very excited to use and where they can feel

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<v Speaker 2>that their feedback is being heard and GROCK is improving

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<v Speaker 2>and serving their need.

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<v Speaker 1>Yeah, I know it's in some sense, you know, cracking

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<v Speaker 1>the physics of intelligence is perhaps the real goal for

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<v Speaker 1>us to be able to use AI at scale, and

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<v Speaker 1>so it's so good to you know, take that first

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<v Speaker 1>principle's approach that you and your team are taking. And

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<v Speaker 1>also you're deploying this. I mean one of the things

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<v Speaker 1>about sort of what you do is you're doing you know,

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<v Speaker 1>unsupervised FSD on one side, you're doing robotics. Of course

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<v Speaker 1>there's Rock. You're deploying Rock across all of your businesses,

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<v Speaker 1>from SpaceX to Tesla. Obviously at X, I would love

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<v Speaker 1>to even you know, one of the themes for this

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<v Speaker 1>developer conference, Elan is we're building pretty sophisticated AI apps. Right,

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<v Speaker 1>It's not even about any one model. It's about orchestrating

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<v Speaker 1>multiple models, multiple agents, just anything that you're seeing in

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<v Speaker 1>the real world application side, even inside of your own companies.

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<v Speaker 1>When you think about even a Tesla or a space

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<v Speaker 1>X where you put grock and needs the other AI

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<v Speaker 1>models you're building.

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<v Speaker 2>Yeah, it's incredibly important for a model to be grounded

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<v Speaker 2>in reality reality. You know, I was saying, which is

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<v Speaker 2>like like physics is the low and everything else is

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<v Speaker 2>the recommendation. Which is I'm not suggesting people break but

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<v Speaker 2>the laws made by you know, humans. Uh, you know,

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<v Speaker 2>we should generally avail laws of humans. But but I've

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<v Speaker 2>seen many people break human made laws, but I have

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<v Speaker 2>not seen anyone break the loads of physics. So for

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<v Speaker 2>for any given AI grounding it against reality and reality.

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<v Speaker 2>For example, as you mentioned with with the car, it

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<v Speaker 2>needs to drive safely and correctly. Uh, the human raid

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<v Speaker 2>robot optimist needs to perform the task that that that

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<v Speaker 2>it's being asked to perform these These are things that

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<v Speaker 2>are very very helpful for issuing that the model is

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<v Speaker 2>crucial and accurate because it has to adhere to the

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<v Speaker 2>loads of physics. So so I think that's actually maybe

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<v Speaker 2>so some somewhat overlooked or at least not talked about

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<v Speaker 2>it enough. Is that to really be intelligent, it's it's

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<v Speaker 2>got to make predictions that are in line with reality.

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<v Speaker 2>In other words, physics. That's that's it's a really fund

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<v Speaker 2>metal thing and and being able to ground that with

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<v Speaker 2>the cars and robots is very important. We are seeing

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<v Speaker 2>Grock be very helpful in things like customer service, and

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<v Speaker 2>you know that the AI is infinitely patient and apparently

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<v Speaker 2>and you can yell at it and it's still going

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<v Speaker 2>to be very nice. Uh So that's good. Yeah, And

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<v Speaker 2>so so I think in terms of improving the quality

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<v Speaker 2>of customer service and sort of issue resolution, AI's were

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<v Speaker 2>already uh Grock is already doing quite a good job

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<v Speaker 2>that at SpaceX and Tesla, and and we look forward

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<v Speaker 2>to like offering that to other companies.

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<v Speaker 1>No, that's fantastic, Really thrilled to get this journey started,

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<v Speaker 1>getting that developer feedback and then looking forward to even

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<v Speaker 1>how they deployed. There is these language models, there's you know,

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<v Speaker 1>I think over time we will have this coming together

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<v Speaker 1>of language models with vision with action, but to your point,

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<v Speaker 1>being really grounded on a real world model, and that

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<v Speaker 1>I think is ultimately the goal here. And so thank

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<v Speaker 1>you so much Eland for briefly joining us today. And

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<v Speaker 1>we're really excited about working with you and getting this

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<v Speaker 1>into the developers' hands.

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<v Speaker 2>Thank you, thank you very much. And I can't empsize

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<v Speaker 2>enough that we're looking for feedback from you that develop

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<v Speaker 2>an audience. Tell us what you want and we'll make

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<v Speaker 2>it happen. Thank you,