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AI & Robotics Revolution: Reinforcement Learning’s Impact on Employment · Earn Your Leisure

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  Speaker 1: pushing your vision forward.
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  Speaker 3: In Nvidia. Few things that stuck out to me.
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  Speaker 4: Jensen was very very adamant a few things, but one
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  Speaker 4: thing in particular was physical AI. Yes and yes in
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  Speaker 4: the in the marketplace. So you asked, what's physical robot
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  Speaker 4: what's physical AI robotics? He brought the robot on stage
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  Speaker 4: with him. In the marketplace, there was tons of robots.
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  Speaker 4: We put it on Instagram, as far as the dog,
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  Speaker 4: the the guy that was doing shout out to Alex
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  Speaker 4: good energy on the check in, the guy that was
• 2:29
  Speaker 4: picking up the stuff in the factories. So there was
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  Speaker 4: two things that Jensen was really big on robotics, and
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  Speaker 4: the other key takeaway that I got was the rational
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  Speaker 4: aspect of artificial intelligence.
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  Speaker 3: That's the new wave. I forget the exactly agent AI.
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  Speaker 4: So, all right, like artificial intelligence as we know it today, right,
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  Speaker 4: let's take chat GBT. You asked it questions and it
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  Speaker 4: gives you answers, right, But if you ask it like
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  Speaker 4: two complex questions, it'll say I can't answer that question,
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  Speaker 4: or that's debatable, or it kind of stares away from asking.
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  Speaker 4: Like if you say, okay, what's the best religion in
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  Speaker 4: the world, they'll say, like, that's a debatable which it
• 3:19
  Speaker 4: is debatable, right, But if I ask a person that
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  Speaker 4: based on their personal beliefs, based on their rational thinking,
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  Speaker 4: based on their upbringing, they'll tell me it's Christianity.
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  Speaker 3: They'll tell me it's Islam, its Buddhism.
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  Speaker 4: That's their personal opinions, and that's how conversations happen with
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  Speaker 4: real people.
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  Speaker 3: Right.
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  Speaker 4: But AI doesn't work like that right now. But the
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  Speaker 4: next iteration of AI is a rational thought process where
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  Speaker 4: it's actually thinking in real time, it's rationalizing situations and
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  Speaker 4: its algorithm and it's giving you answers based off of
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  Speaker 4: not only it's intelligence, but also it's rational mind. That's
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  Speaker 4: what's kind of separating humans from AI right now, because
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  Speaker 4: they already have intelligence, but they don't have the emotional intelligence,
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  Speaker 4: the rational the how to talk. That's the next iteration
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  Speaker 4: of AIS. So if you think about it, like, what
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  Speaker 4: that's going to do is lower the value of education
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  Speaker 4: because like let's say, like right now, lawyers get paid
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  Speaker 4: from a scarcity of knowledge that only a select few
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  Speaker 4: people in the world have. Like, if you have a
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  Speaker 4: law degree, you're in a select few group of people
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  Speaker 4: in the total sum of the world, right, And that
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  Speaker 4: knowledge that you've acquired over eight years and then working,
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  Speaker 4: that's made your education scarce, and that's major knowledge scarce,
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  Speaker 4: which makes.
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  Speaker 3: Your value high.
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  Speaker 2: Absolutely.
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  Speaker 4: Now if everybody has that same knowledge and that same
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  Speaker 4: rational thought to be able to not only say this
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  Speaker 4: contract is good or bad, but no, this is what
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  Speaker 4: we need is how we need to negotiate this contract. Well, now,
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  Speaker 4: the value of a lawyers is really nothing because I'm
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  Speaker 4: a lawyer, you're a lawyer, Ian's a lawyer.
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  Speaker 3: Everybody is a lawyer.
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  Speaker 4: If we have the artificial intelligence lawyer, right, So he
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  Speaker 4: gave the example as far as going and the dinner
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  Speaker 4: and at Thanksgiving, and it's like, now it's like, okay,
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  Speaker 4: if I'm gonna have my spouse, the like, okay, I
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  Speaker 4: got eight people, sit me at a table right now,
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  Speaker 4: It'll just give you a random seating chart. But the
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  Speaker 4: new version will think about all scenarios. It will ask
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  Speaker 4: you questions, and it will see people accordingly based on
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  Speaker 4: their opinion. Stop breaking protocol, man, stop breaking protocol.
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  Speaker 2: Say keep going, keep going, keep going, we go down.
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  Speaker 4: So that was that was a big, big thing that
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  Speaker 4: he really pushed. And he and you could talk about
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  Speaker 4: the technical side as far as you're gonna make it
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  Speaker 4: it's smarter, and it's it's using more gigs and the
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  Speaker 4: whole nine. But for me, those are the two things
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  Speaker 4: that really stuck out to me because it was like,
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  Speaker 4: everybody's going to have a robot mm hmm. The number
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  Speaker 4: one job in the world for men? Put in chat,
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  Speaker 4: what's the number one job that was a gym? What's
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  Speaker 4: the number one job in the world for men as
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  Speaker 4: far as that employs the most men globally? What's the
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  Speaker 4: number one job in the world. And Alex you could
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  Speaker 4: appreciate this, what's the number one job in the world
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  Speaker 4: that employs the most men globally.
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  Speaker 3: Let's see if anybody gets.
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  Speaker 1: That was one of those like you learn something new
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  Speaker 1: every day, which brought me to my point of what
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  Speaker 1: I took away and how scary that is.
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  Speaker 2: Yeah, I'll chime in, and then you can chime in
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  Speaker 2: the mind. Yeah, no, let them do this.
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  Speaker 4: The number one job in the world for men is drivers,
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  Speaker 4: cab drivers, uber eats truck drivers.
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  Speaker 2: Anywhere in the world.
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  Speaker 4: You think about it, like, you don't need a skill
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  Speaker 4: set to be a driver, you just need a car, right,
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  Speaker 4: and you could go to South Africa, you can go
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  Speaker 4: to India, there's always a cab driver. There's always like
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  Speaker 4: that's the number one job for men globally. Yep, that's
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  Speaker 4: going to be a race pretty soon. Yeas driving toamis
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  Speaker 4: drives a nothing. They have their partnership with gm Atonomus.
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  Speaker 4: Driving is going to take hundreds of millions of jobs.
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  Speaker 4: So imagine all of those people. This is the number
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  Speaker 4: one employer for men is a driver. And if we
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  Speaker 4: don't need a driver because we have a totomist driving,
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  Speaker 4: what does that do to all the number one job
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  Speaker 4: for men in the world.
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  Speaker 1: I think and just to introduct just briefly, the autonomous
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  Speaker 1: piece that they displayed was completely different than what we
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  Speaker 1: saw like in a tussa where you get in a
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  Speaker 1: car and it's kind of scanning your surroundings to make
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  Speaker 1: sure that there's a car is not too close to
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  Speaker 1: you on your right or your left, or did somebody
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  Speaker 1: walk and there's a dog, there's a sidewalk.
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  Speaker 2: This was something completely different what we saw.
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  Speaker 1: This actually was understanding the physical environment based on any scenario, right,
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  Speaker 1: And so it would scan the area and then it
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  Speaker 1: would tell you, based on the weather perception, what how
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  Speaker 1: you should how it should drive if it snowed, how
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  Speaker 1: to drive to a sleep if it was. It studied
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  Speaker 1: the weather patterns and so now it's prepared to drive
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  Speaker 1: in any condition. Whereas an average human there's going to
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  Speaker 1: be human error. This is preparing for the human error
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  Speaker 1: before it even happens.
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  Speaker 2: Right.
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  Speaker 1: So like that piece of understanding the physical world, this
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  Speaker 1: is a game, shoed.
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  Speaker 2: I'm not sure you finished. Women to want me to
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  Speaker 2: go yeah, yeah, So like this is literally in my notes.
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  Speaker 1: So, Agent Ki, was that piece you were talking about
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  Speaker 1: and I saw some people put in the chat it's
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  Speaker 1: that reasoning piece right, so able to ask the question,
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  Speaker 1: but it's also able to solve complex problems on its own,
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  Speaker 1: which is we're not at that space right now, right,
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  Speaker 1: But that is the first thing. The physical AI, like
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  Speaker 1: you said, the robotics. I think the piece that he
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  Speaker 1: said is that that physical AI with reinforcement learning, and
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  Speaker 1: so that's when the machine starts to learn and make
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  Speaker 1: decisions in the environment right to maximize the reward. So
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  Speaker 1: like this machine is now doing these things and it's like, oh,
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  Speaker 1: that's how's supposed to do it, trial and error.
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  Speaker 2: This is not supposed to do it. That's how I'm
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  Speaker 2: not supposed to do it.
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  Speaker 1: It was like when we had we thought of robotics,
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  Speaker 1: if they held a ball in their hand and they
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  Speaker 1: dropped it, it wasn't like where did the ball go?
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  Speaker 1: The ball was just no longer there. The reinforce learning
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  Speaker 1: is like, oh, the ball dropped. It must have fell down.
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  Speaker 1: I have to now find it, right, So imagine like
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  Speaker 1: it's that type of robotics. The interesting thing that he
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  Speaker 1: said is like, yes, tbt's are important, right, agents are
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  Speaker 1: going to be important. He said, the biggest piece and
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  Speaker 1: the biggest opportunity left in the AI puzzle is when
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  Speaker 1: physical AI and reinforcement learning are combined, which is when
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  Speaker 1: it introduced the robotics that would come inside your home.
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  Speaker 1: So we were I mean, we were just sitting there
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  Speaker 1: thinking to each other, like wait, hold up, what we're
• 9:40
  Speaker 1: talking about? Big u on over here, and like they're
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  Speaker 1: putting robots in people's homes, and they're trying to rent
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  Speaker 1: these robots and they already got the price points.
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  Speaker 2: So that was the first piece.
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  Speaker 1: And then we got to go into the exhibit hall
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  Speaker 1: and just like you said, from a technical standpoint, and
• 9:54
  Speaker 1: go watch we so like people talk about blackwell, like
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  Speaker 1: we actually saw it like in front of us, right
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  Speaker 1: on the table, Like we got to pick and look
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  Speaker 1: at it, and.
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  Speaker 2: Like we saw what it looked like.
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  Speaker 1: And so obviously then the mind starts turning right Like
• 10:12
  Speaker 1: I'm like, there's a few times in life where you
• 10:15
  Speaker 1: feel validated, right, and like the information felt validated, And
• 10:18
  Speaker 1: so I'm asking questions. I'm just trying to pride from engineers,
• 10:21
  Speaker 1: and I'm like, what's that was that? Was that knowing
• 10:23
  Speaker 1: what I'm looking for the answer in mind? And I
• 10:26
  Speaker 1: was like, okay, here all the racks. Who's making these racks? Hey,
• 10:30
  Speaker 1: got my answer. All right, If that's the GPU and
• 10:34
  Speaker 1: this is liquid cooling, who's providing the liquor cooling for this?
• 10:38
  Speaker 2: All right? Bet? If that's the GPU, who's doing the
• 10:41
  Speaker 2: storage for that? Oh?
• 10:42
  Speaker 1: Bet going to vendor markle. You see all the players.
• 10:45
  Speaker 1: All the players are there. So if I'm talking about Micron,
• 10:48
  Speaker 1: Micron is there. If I'm talking about Vertiv in terms
• 10:50
  Speaker 1: of liquor cooling, vertive is there. If we're talking about
• 10:53
  Speaker 1: super Micro, super Micro's there. All these You're starting to
• 10:58
  Speaker 1: see all these players and it's like, oh, this makes sense.
• 11:01
  Speaker 1: And so when we talk about yes, uncertainty is key
• 11:04
  Speaker 1: to how the market moves.
• 11:06
  Speaker 2: AI is still the story.
• 11:08
  Speaker 1: And even when he spoke about deep Seek and he
• 11:11
  Speaker 1: was like, look, you guys got this thing one hundred
• 11:13
  Speaker 1: percent wrong, just because they had it and they found
• 11:15
  Speaker 1: they're still going to need compute and it's still going
• 11:18
  Speaker 1: to take a large amount of money to start to
• 11:20
  Speaker 1: getting these compute these GPUs out, I'm like, damn. Then
• 11:25
  Speaker 1: the last piece was like, how are people going to
• 11:28
  Speaker 1: even compete at this level this? Not only are they saying, hey,
• 11:31
  Speaker 1: here's our product for twenty twenty five, here's our product
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  Speaker 1: for twenty twenty six with black Belt Ultra. Here's our
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  Speaker 1: six with Ruben, here's our product for twenty twenty seven,
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  Speaker 1: Ruben Ultra Yo, here's our product for.
• 11:45
  Speaker 2: Twenty twenty eight, twenty eight Fineman.
• 11:49
  Speaker 1: He's already three years ahead in the products right with
• 11:52
  Speaker 1: people are just trying to make a product to compete for.
• 11:54
  Speaker 4: GPUs that are three times ahead of their nearest competitor.
• 11:58
  Speaker 1: He's three years ahead. I'm talking to the people. I'm
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  Speaker 1: like your houses work. They were like, yo, We're in
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