May 20, 2025 • 6 mins
Conversation with Elon Musk and Satya Nadella at Microsoft Build 2025.
#ElonMusk #SatyaNadella
Source: Microsoft
Follow me on X https://x.com/Astronautman627?...
#ElonMusk #SatyaNadella
Source: Microsoft
Follow me on X https://x.com/Astronautman627?...
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Thank you so much, Lon for being here at build.
I know you started off as an intern at Microsoft,
you were a Windows developer, and of course you're a
big PC gamer. Still, you want to just talk about
you in your early days with Windows and the kinds
of things you build.
Speaker 2 (00:17):
Yeah, well, actually started before Windows with us. I had
one of the early IBM PCs with MS DOS and
I think I had like a one hundred and twenty eight
K in the beginning, and then at double T two
fifty six K, which felt like a liar, so I yeah,
pro programmed video games indus and then in Windows remember
(00:41):
Windows three point one.
Speaker 1 (00:43):
Yeah, no, it's wonderful. I mean even at the last
time I chatted with you, you were talking all about everything,
the intricacies of active directory, and so it's fantastic to
have you at our developer conference. Obviously, the exciting thing
for us is to be able to launch grock on Azure.
I know you have a deep vision for what AI
needs to be, and that's what got you to get
(01:05):
this billed. It's a family of models that are both
response and reasoning models, and you have a very exciting roadmap.
You want to just tell us a little bit about
sort of your vision the capability you're pushing on both
capability and efficiency, So maybe you can just talk about
a little bit of that.
Speaker 2 (01:20):
Sure. So yeah, with with GROC, especially with Rock three
point five that is about to be released, it's it's
trying to reason from first principles, so apply kind of
the the tools of physics to thinking. So if you're
trying to get to fundamental truths, you you try you
boil things down to the axiomatic elements that are most
(01:41):
likely to be correct, and then you reason up from there,
and then you can test your conclusions against those axiomatic elements.
And you know, in physics, if if you violate conservation
of energy or momentum, then you're either going to get
a Nobel price or you're you're wrong, and you're certainly
wrong basically. So so the the that's really the focus
of Rock three point five is uh, sort of I
(02:05):
find a fundamental physics and applying physics tools across all
lines of reasoning and to aspire to truth with minimal error.
Like there's always gonna be some mistakes that are made,
but we aim to get to truth with acknowledged error,
but minimize that error over time, and I think that's
(02:29):
actually extremely important for AI safety. So I've put a
lot for a long time about AI safety and wealth.
Book conclusion is the old maximum that honesty is the
best policy. It really really is for safety. But any
want to have size. You know, we we haven't will
make mistakes, but we aspire to correct them very quickly,
(02:51):
and we are very much looking forward to feedback from
the developer community to say like, what do you need?
Where are we wrong? Cocker, we make it better, and
to have Rock be something that the developer community community
is very excited to use and where they can feel
that their feedback is being heard and GROCK is improving
(03:12):
and serving their need.
Speaker 1 (03:13):
Yeah, I know it's in some sense, you know, cracking
the physics of intelligence is perhaps the real goal for
us to be able to use AI at scale, and
so it's so good to you know, take that first
principle's approach that you and your team are taking. And
also you're deploying this. I mean one of the things
about sort of what you do is you're doing you know,
(03:34):
unsupervised FSD on one side, you're doing robotics. Of course
there's Rock. You're deploying Rock across all of your businesses,
from SpaceX to Tesla. Obviously at X, I would love
to even you know, one of the themes for this
developer conference, Elan is we're building pretty sophisticated AI apps. Right,
It's not even about any one model. It's about orchestrating
(03:55):
multiple models, multiple agents, just anything that you're seeing in
the real world application side, even inside of your own companies.
When you think about even a Tesla or a space
X where you put grock and needs the other AI
models you're building.
Speaker 2 (04:10):
Yeah, it's incredibly important for a model to be grounded
in reality reality. You know, I was saying, which is
like like physics is the low and everything else is
the recommendation. Which is I'm not suggesting people break but
the laws made by you know, humans. Uh, you know,
we should generally avail laws of humans. But but I've
(04:31):
seen many people break human made laws, but I have
not seen anyone break the loads of physics. So for
for any given AI grounding it against reality and reality.
For example, as you mentioned with with the car, it
needs to drive safely and correctly. Uh, the human raid
robot optimist needs to perform the task that that that
(04:53):
it's being asked to perform these These are things that
are very very helpful for issuing that the model is
crucial and accurate because it has to adhere to the
loads of physics. So so I think that's actually maybe
so some somewhat overlooked or at least not talked about
it enough. Is that to really be intelligent, it's it's
(05:15):
got to make predictions that are in line with reality.
In other words, physics. That's that's it's a really fund
metal thing and and being able to ground that with
the cars and robots is very important. We are seeing
Grock be very helpful in things like customer service, and
you know that the AI is infinitely patient and apparently
(05:37):
and you can yell at it and it's still going
to be very nice. Uh So that's good. Yeah, And
so so I think in terms of improving the quality
of customer service and sort of issue resolution, AI's were
already uh Grock is already doing quite a good job
that at SpaceX and Tesla, and and we look forward
(05:59):
to like offering that to other companies.
Speaker 1 (06:02):
No, that's fantastic, Really thrilled to get this journey started,
getting that developer feedback and then looking forward to even
how they deployed. There is these language models, there's you know,
I think over time we will have this coming together
of language models with vision with action, but to your point,
being really grounded on a real world model, and that
(06:22):
I think is ultimately the goal here. And so thank
you so much Eland for briefly joining us today. And
we're really excited about working with you and getting this
into the developers' hands.
Speaker 2 (06:31):
Thank you, thank you very much. And I can't empsize
enough that we're looking for feedback from you that develop
an audience. Tell us what you want and we'll make
it happen. Thank you,
Thank you so much, Lon for being here at build.
I know you started off as an intern at Microsoft,
you were a Windows developer, and of course you're a
big PC gamer. Still, you want to just talk about
you in your early days with Windows and the kinds
of things you build.
Speaker 2 (00:17):
Yeah, well, actually started before Windows with us. I had
one of the early IBM PCs with MS DOS and
I think I had like a one hundred and twenty eight
K in the beginning, and then at double T two
fifty six K, which felt like a liar, so I yeah,
pro programmed video games indus and then in Windows remember
(00:41):
Windows three point one.
Speaker 1 (00:43):
Yeah, no, it's wonderful. I mean even at the last
time I chatted with you, you were talking all about everything,
the intricacies of active directory, and so it's fantastic to
have you at our developer conference. Obviously, the exciting thing
for us is to be able to launch grock on Azure.
I know you have a deep vision for what AI
needs to be, and that's what got you to get
(01:05):
this billed. It's a family of models that are both
response and reasoning models, and you have a very exciting roadmap.
You want to just tell us a little bit about
sort of your vision the capability you're pushing on both
capability and efficiency, So maybe you can just talk about
a little bit of that.
Speaker 2 (01:20):
Sure. So yeah, with with GROC, especially with Rock three
point five that is about to be released, it's it's
trying to reason from first principles, so apply kind of
the the tools of physics to thinking. So if you're
trying to get to fundamental truths, you you try you
boil things down to the axiomatic elements that are most
(01:41):
likely to be correct, and then you reason up from there,
and then you can test your conclusions against those axiomatic elements.
And you know, in physics, if if you violate conservation
of energy or momentum, then you're either going to get
a Nobel price or you're you're wrong, and you're certainly
wrong basically. So so the the that's really the focus
of Rock three point five is uh, sort of I
(02:05):
find a fundamental physics and applying physics tools across all
lines of reasoning and to aspire to truth with minimal error.
Like there's always gonna be some mistakes that are made,
but we aim to get to truth with acknowledged error,
but minimize that error over time, and I think that's
(02:29):
actually extremely important for AI safety. So I've put a
lot for a long time about AI safety and wealth.
Book conclusion is the old maximum that honesty is the
best policy. It really really is for safety. But any
want to have size. You know, we we haven't will
make mistakes, but we aspire to correct them very quickly,
(02:51):
and we are very much looking forward to feedback from
the developer community to say like, what do you need?
Where are we wrong? Cocker, we make it better, and
to have Rock be something that the developer community community
is very excited to use and where they can feel
that their feedback is being heard and GROCK is improving
(03:12):
and serving their need.
Speaker 1 (03:13):
Yeah, I know it's in some sense, you know, cracking
the physics of intelligence is perhaps the real goal for
us to be able to use AI at scale, and
so it's so good to you know, take that first
principle's approach that you and your team are taking. And
also you're deploying this. I mean one of the things
about sort of what you do is you're doing you know,
(03:34):
unsupervised FSD on one side, you're doing robotics. Of course
there's Rock. You're deploying Rock across all of your businesses,
from SpaceX to Tesla. Obviously at X, I would love
to even you know, one of the themes for this
developer conference, Elan is we're building pretty sophisticated AI apps. Right,
It's not even about any one model. It's about orchestrating
(03:55):
multiple models, multiple agents, just anything that you're seeing in
the real world application side, even inside of your own companies.
When you think about even a Tesla or a space
X where you put grock and needs the other AI
models you're building.
Speaker 2 (04:10):
Yeah, it's incredibly important for a model to be grounded
in reality reality. You know, I was saying, which is
like like physics is the low and everything else is
the recommendation. Which is I'm not suggesting people break but
the laws made by you know, humans. Uh, you know,
we should generally avail laws of humans. But but I've
(04:31):
seen many people break human made laws, but I have
not seen anyone break the loads of physics. So for
for any given AI grounding it against reality and reality.
For example, as you mentioned with with the car, it
needs to drive safely and correctly. Uh, the human raid
robot optimist needs to perform the task that that that
(04:53):
it's being asked to perform these These are things that
are very very helpful for issuing that the model is
crucial and accurate because it has to adhere to the
loads of physics. So so I think that's actually maybe
so some somewhat overlooked or at least not talked about
it enough. Is that to really be intelligent, it's it's
(05:15):
got to make predictions that are in line with reality.
In other words, physics. That's that's it's a really fund
metal thing and and being able to ground that with
the cars and robots is very important. We are seeing
Grock be very helpful in things like customer service, and
you know that the AI is infinitely patient and apparently
(05:37):
and you can yell at it and it's still going
to be very nice. Uh So that's good. Yeah, And
so so I think in terms of improving the quality
of customer service and sort of issue resolution, AI's were
already uh Grock is already doing quite a good job
that at SpaceX and Tesla, and and we look forward
(05:59):
to like offering that to other companies.
Speaker 1 (06:02):
No, that's fantastic, Really thrilled to get this journey started,
getting that developer feedback and then looking forward to even
how they deployed. There is these language models, there's you know,
I think over time we will have this coming together
of language models with vision with action, but to your point,
being really grounded on a real world model, and that
(06:22):
I think is ultimately the goal here. And so thank
you so much Eland for briefly joining us today. And
we're really excited about working with you and getting this
into the developers' hands.
Speaker 2 (06:31):
Thank you, thank you very much. And I can't empsize
enough that we're looking for feedback from you that develop
an audience. Tell us what you want and we'll make
it happen. Thank you,
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