Speaker 1: Thank you so much, Lon for being here at build.
Speaker 1: I know you started off as an intern at Microsoft,
Speaker 1: you were a Windows developer, and of course you're a
Speaker 1: big PC gamer. Still, you want to just talk about
Speaker 1: you in your early days with Windows and the kinds
Speaker 1: of things you build.
Speaker 2: Yeah, well, actually started before Windows with us. I had
Speaker 2: one of the early IBM PCs with MS DOS and
Speaker 2: I think I had like a one hundred and twenty eight
Speaker 2: K in the beginning, and then at double T two
Speaker 2: fifty six K, which felt like a liar, so I yeah,
Speaker 2: pro programmed video games indus and then in Windows remember
Speaker 2: Windows three point one.
Speaker 1: Yeah, no, it's wonderful. I mean even at the last
Speaker 1: time I chatted with you, you were talking all about everything,
Speaker 1: the intricacies of active directory, and so it's fantastic to
Speaker 1: have you at our developer conference. Obviously, the exciting thing
Speaker 1: for us is to be able to launch grock on Azure.
Speaker 1: I know you have a deep vision for what AI
Speaker 1: needs to be, and that's what got you to get
Speaker 1: this billed. It's a family of models that are both
Speaker 1: response and reasoning models, and you have a very exciting roadmap.
Speaker 1: You want to just tell us a little bit about
Speaker 1: sort of your vision the capability you're pushing on both
Speaker 1: capability and efficiency, So maybe you can just talk about
Speaker 1: a little bit of that.
Speaker 2: Sure. So yeah, with with GROC, especially with Rock three
Speaker 2: point five that is about to be released, it's it's
Speaker 2: trying to reason from first principles, so apply kind of
Speaker 2: the the tools of physics to thinking. So if you're
Speaker 2: trying to get to fundamental truths, you you try you
Speaker 2: boil things down to the axiomatic elements that are most
Speaker 2: likely to be correct, and then you reason up from there,
Speaker 2: and then you can test your conclusions against those axiomatic elements.
Speaker 2: And you know, in physics, if if you violate conservation
Speaker 2: of energy or momentum, then you're either going to get
Speaker 2: a Nobel price or you're you're wrong, and you're certainly
Speaker 2: wrong basically. So so the the that's really the focus
Speaker 2: of Rock three point five is uh, sort of I
Speaker 2: find a fundamental physics and applying physics tools across all
Speaker 2: lines of reasoning and to aspire to truth with minimal error.
Speaker 2: Like there's always gonna be some mistakes that are made,
Speaker 2: but we aim to get to truth with acknowledged error,
Speaker 2: but minimize that error over time, and I think that's
Speaker 2: actually extremely important for AI safety. So I've put a
Speaker 2: lot for a long time about AI safety and wealth.
Speaker 2: Book conclusion is the old maximum that honesty is the
Speaker 2: best policy. It really really is for safety. But any
Speaker 2: want to have size. You know, we we haven't will
Speaker 2: make mistakes, but we aspire to correct them very quickly,
Speaker 2: and we are very much looking forward to feedback from
Speaker 2: the developer community to say like, what do you need?
Speaker 2: Where are we wrong? Cocker, we make it better, and
Speaker 2: to have Rock be something that the developer community community
Speaker 2: is very excited to use and where they can feel
Speaker 2: that their feedback is being heard and GROCK is improving
Speaker 2: and serving their need.
Speaker 1: Yeah, I know it's in some sense, you know, cracking
Speaker 1: the physics of intelligence is perhaps the real goal for
Speaker 1: us to be able to use AI at scale, and
Speaker 1: so it's so good to you know, take that first
Speaker 1: principle's approach that you and your team are taking. And
Speaker 1: also you're deploying this. I mean one of the things
Speaker 1: about sort of what you do is you're doing you know,
Speaker 1: unsupervised FSD on one side, you're doing robotics. Of course
Speaker 1: there's Rock. You're deploying Rock across all of your businesses,
Speaker 1: from SpaceX to Tesla. Obviously at X, I would love
Speaker 1: to even you know, one of the themes for this
Speaker 1: developer conference, Elan is we're building pretty sophisticated AI apps. Right,
Speaker 1: It's not even about any one model. It's about orchestrating
Speaker 1: multiple models, multiple agents, just anything that you're seeing in
Speaker 1: the real world application side, even inside of your own companies.
Speaker 1: When you think about even a Tesla or a space
Speaker 1: X where you put grock and needs the other AI
Speaker 1: models you're building.
Speaker 2: Yeah, it's incredibly important for a model to be grounded
Speaker 2: in reality reality. You know, I was saying, which is
Speaker 2: like like physics is the low and everything else is
Speaker 2: the recommendation. Which is I'm not suggesting people break but
Speaker 2: the laws made by you know, humans. Uh, you know,
Speaker 2: we should generally avail laws of humans. But but I've
Speaker 2: seen many people break human made laws, but I have
Speaker 2: not seen anyone break the loads of physics. So for
Speaker 2: for any given AI grounding it against reality and reality.
Speaker 2: For example, as you mentioned with with the car, it
Speaker 2: needs to drive safely and correctly. Uh, the human raid
Speaker 2: robot optimist needs to perform the task that that that
Speaker 2: it's being asked to perform these These are things that
Speaker 2: are very very helpful for issuing that the model is
Speaker 2: crucial and accurate because it has to adhere to the
Speaker 2: loads of physics. So so I think that's actually maybe
Speaker 2: so some somewhat overlooked or at least not talked about
Speaker 2: it enough. Is that to really be intelligent, it's it's
Speaker 2: got to make predictions that are in line with reality.
Speaker 2: In other words, physics. That's that's it's a really fund
Speaker 2: metal thing and and being able to ground that with
Speaker 2: the cars and robots is very important. We are seeing
Speaker 2: Grock be very helpful in things like customer service, and
Speaker 2: you know that the AI is infinitely patient and apparently
Speaker 2: and you can yell at it and it's still going
Speaker 2: to be very nice. Uh So that's good. Yeah, And
Speaker 2: so so I think in terms of improving the quality
Speaker 2: of customer service and sort of issue resolution, AI's were
Speaker 2: already uh Grock is already doing quite a good job
Speaker 2: that at SpaceX and Tesla, and and we look forward
Speaker 2: to like offering that to other companies.
Speaker 1: No, that's fantastic, Really thrilled to get this journey started,
Speaker 1: getting that developer feedback and then looking forward to even
Speaker 1: how they deployed. There is these language models, there's you know,
Speaker 1: I think over time we will have this coming together
Speaker 1: of language models with vision with action, but to your point,
Speaker 1: being really grounded on a real world model, and that
Speaker 1: I think is ultimately the goal here. And so thank
Speaker 1: you so much Eland for briefly joining us today. And
Speaker 1: we're really excited about working with you and getting this
Speaker 1: into the developers' hands.
Speaker 2: Thank you, thank you very much. And I can't empsize
Speaker 2: enough that we're looking for feedback from you that develop
Speaker 2: an audience. Tell us what you want and we'll make
Speaker 2: it happen. Thank you,