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Speaker 1: Welcome. It is Verdict with Center, Ted Cruz, Ben Ferguson

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Speaker 1: with you. And today is a very special day as

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Speaker 1: we are doing part one of our two part conversation

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Speaker 1: at the White House with Elon Musk.

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Speaker 2: Now.

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Speaker 1: Elon is going to break news during today and Wednesday's

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Speaker 1: show on Doge and some credible, shocking information about corruption

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Speaker 1: within our government. I want to make sure you hit

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Speaker 1: that subscribe button, that auto download button right now so

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Speaker 1: you do not miss part two on Wednesday. Also, please

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Speaker 1: help this go viral as we're exposing government waste by

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Speaker 1: sharing this episode on any social media platform that you

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Speaker 1: are on. But before we get to that, after more

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Speaker 1: than a year of war, tear and pain in Israel,

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Speaker 1: the need for security essentials and support for first responders

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Speaker 1: is still critical. Even in times of ceasefire. Israel must

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Speaker 1: be prepared for the next attack wherever it may come from.

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Speaker 1: As Israel is surrounded by enemies on all sides. That

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Speaker 1: is where the International Fellowship of Christians and Jews and

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Speaker 1: you come in. They are the ones that help give

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Speaker 1: the support that is needed and the people of Israel

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Speaker 1: need now more than ever before. Life saving security essentials

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Speaker 1: and your gift today will help save lives by providing

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Speaker 1: bomb shelters, armored security vehicles and ambulances, firefighting equipment, flat

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Speaker 1: jackets and bulletproof vests, and so much more. You're generous

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Speaker 1: donation today will help ensure the people of Israel are

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Speaker 1: safe and secure in the days to come. So give

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Speaker 1: a gift to bless Israel and our people by visiting

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Speaker 1: support IFCJ dot org. That's one word, support IFCJ dot org.

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Speaker 1: Or call eight a eight for eight eight IFCJ. That's

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Speaker 1: eight eight eight four eight eight IFCJ eight and eight

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Speaker 1: four eight eight four three two five or support IFCJ

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Speaker 1: dot org. So, without any further ado, here is part

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Speaker 1: one of our sit down conversation at the White House

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Speaker 1: with Elon Musk.

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Speaker 3: Well, we're in the White House. What right now? And

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Speaker 3: we're here with my friend Elon Musk, who really has

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Speaker 3: not been doing much of anything, has not made any

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Speaker 3: news is and nobody has noticed. Yeah the impact. Welcome Elon.

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Speaker 2: Thank you.

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Speaker 3: Holy crap.

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Speaker 2: Oh yes wow. Let me just say, never a dull moment.

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Speaker 3: Never a dull moment. The first fifty days the president

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Speaker 3: has spent in office over the top, and the first

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Speaker 3: fifty days you've spent. I don't think there's ever been

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Speaker 3: anyone to have an impact the way you have. At

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Speaker 3: the beginning, let me start with a question. You know

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Speaker 3: a lot about which was worse the mess you found

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Speaker 3: at Twitter or the mess you found in the federal government.

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Speaker 2: Well, it's hard to compete with the federal government.

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Speaker 3: What's surprised you about the federal government? I assume you

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Speaker 3: came in and assumed it was bad. Is it worse

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Speaker 3: than you expected?

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Speaker 4: It is worse than I expected. But on the plus side,

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Speaker 4: that means.

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Speaker 2: This more oportunity for improvement.

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Speaker 4: So look, if you look on the right side, there's

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Speaker 4: there's actually a lot of opportunity for improvement in federal

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Speaker 4: goverment expenditures because it's so bad. If it was a

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Speaker 4: well run ship, it would be very difficult to improve.

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Speaker 4: So like, but so now it's like if people say, well,

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Speaker 4: how how do you figure out how to save money

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Speaker 4: in the federal government? Well, it's like being in a

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Speaker 4: room where the walls, the roof, and the floor are

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Speaker 4: old targets any direction and you're kindness, Wow, yeah, I'm

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Speaker 4: sure you would agree.

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Speaker 3: So a lot of folks have talked about like.

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Speaker 4: Like you count right, this is going to any direction.

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Speaker 3: A lot of the crazy expenditures, things like like two

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Speaker 3: million bucks for sex change surgeries in Guatemala, an essential

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Speaker 3: you know, transgendered mice and and sesame street in Iraq.

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Speaker 3: A lot of that has gotten a tension. But some

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Speaker 3: of the stuff you've told me about, but like tell

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Speaker 3: us about computer licenses and government agencies.

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Speaker 4: Yeah, so most of what dog just finding. You don't

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Speaker 4: need to use shlock homes. It's very obvious basic stuff.

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Speaker 4: So in every government department, I say every because we've

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Speaker 4: not yet found a single exception. There are far too

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Speaker 4: many software licenses and media subscriptions, meaning many more software

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Speaker 4: licenses and media subscriptions than there are humans in the department.

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Speaker 3: Like you were saying, like an agency with fifteen thousand

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Speaker 3: people might have thirty thousand licenses. Yes, and even of

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Speaker 3: the fifteen thousand employees, a good chunk of them hadn't

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Speaker 3: used the license, had never logged on or used the application.

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Speaker 4: Yes, we found entire situations of software licenses or media

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Speaker 4: subscriptions where there were zero logins.

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Speaker 3: So it had and yet we were paying for it.

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Speaker 4: Yes, the government's paying for thousands of licenses of software

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Speaker 4: or media subscriptions and no one had ever logged in

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Speaker 4: even once.

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Speaker 3: Or credit cards. You found the same thing with government

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Speaker 3: credit cards.

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Speaker 4: We found that there are twice as many credit cards

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Speaker 4: there are humans. And I still don't have a good

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Speaker 4: explanation for why this is the case. And these are

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Speaker 4: ten thousand dollars limit cards. So it's a lot of money.

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Speaker 1: Is it incompetent that you're finding or is this like

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Speaker 1: the biggest money laundering scheme in the history of the

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Speaker 1: world that you're finding.

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Speaker 4: Okay, I think it's mostly if you say, look, what's

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Speaker 4: the waste to fraud ratio? Yeah, in my opinion, it's

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Speaker 4: it's like eighty percent wasst twenty percent fraud. But you

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Speaker 4: do have these sort of gray areas. For example, example

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Speaker 4: be so we saw a lot of payments going out

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Speaker 4: of treasury that had no payment code and no explanation

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Speaker 4: for the payment, and then we're we're trying to figure

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Speaker 4: out what that payment is and we'd see that Okay,

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Speaker 4: that contract was supposed to be shut off, but someone

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Speaker 4: forgot to shut off that that contract and so the

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Speaker 4: company kept getting money. Wow, Now is that waste or

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Speaker 4: fraud both both?

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Speaker 1: Yeah, you're not supposed to get you're not supposed to

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Speaker 1: get it, but you but the government sent it to

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Speaker 1: you and nobody from the government asked for it back.

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Speaker 4: Take, for example, the one the one point nine billion

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Speaker 4: dollars given to Stacy Abrams.

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Speaker 3: Yeah, fakengo, utter insanity. Explain the story that's that's that's

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Speaker 3: just corrupt. I think that's paying off cronies at that point.

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Speaker 4: Yeah.

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Speaker 2: Yeah.

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Speaker 3: And by the way, she knew, like when you get

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Speaker 3: two billion dollars, you don't miss that. That's not on accident.

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Speaker 4: That's allegedly it was for like uh, you know, environmentally

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Speaker 4: friendly appliances or something. And they've given like like one

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Speaker 4: hundred appliances so far for two billion dollars.

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Speaker 3: It's very expensive toast. That's some zo fridge. It's nice.

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Speaker 4: It is obviously one of the biggest scam portholes we've uncovered,

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Speaker 4: which is really crazy, is uh is that is that

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Speaker 4: the government can give money to a so called nonprofit

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Speaker 4: with with very few controls and then that and there's

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Speaker 4: there's no auditing subsequently of that nonprofit, so there's no

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Speaker 4: So this is where with the you know what point

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Speaker 4: nine billion of Stacey Arams, who's who's that they didn't

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Speaker 4: give themselves extremely lavished like insane salaries, expense everything yep,

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Speaker 4: to the to the nonprofit you know, buy jets and

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Speaker 4: homes and all sorts of.

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Speaker 3: Things, live like kings and queens.

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Speaker 2: Yes on the taxpayer, dont correct. You mentioned this is

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Speaker 2: happening at scale. It's not just one or two. We're

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Speaker 2: seeing this everywhere.

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Speaker 3: Now, one of the things you told me about is

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Speaker 3: it's great is what you call say magic money computers. Well,

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Speaker 3: so tell us about it, because I've never heard of

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Speaker 3: that until you brought that up.

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Speaker 4: Okay, So you may think that these the government computers

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Speaker 4: like all talk to each other, they synchronize, they add

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Speaker 4: up what funds are going somewhere, and it's you know,

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Speaker 4: it's coherent that that that the you know, there's and

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Speaker 4: that and that. The numbers, for example, that you're presented

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Speaker 4: as a senator, Yeah, are actually the.

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Speaker 3: Real numbers in one would think, well, I would think

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Speaker 3: they're not.

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Speaker 2: Yeah, okay, I mean.

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Speaker 4: They're not totally wrong, but they're probably off by five

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Speaker 4: percent or ten percent in some cases. So I call

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Speaker 4: it magic money computer. Any computer which can just make

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Speaker 4: money out of thin air best magic money.

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Speaker 3: So how does that work?

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Speaker 2: It just issues payments, and you.

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Speaker 3: Said something like eleven of these computers of treasury that

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Speaker 3: are that are sending out trillions in payments.

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Speaker 4: They're mostly a Treasury, some are with the sum at HHS,

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Speaker 4: some at there's one as one of two as States,

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Speaker 4: there's summit at DoD. I think we found now fourteen

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Speaker 4: magic money computers.

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Speaker 3: Gene.

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Speaker 2: Okay, they just send money out of nothing.

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Speaker 3: You have an ability to see where leverage points are

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Speaker 3: and how things actually happen. So I remember back I

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Speaker 3: think it was September October of this year, before the election.

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Speaker 3: We didn't know who was going to win, and I

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Speaker 3: was at your house in Austin. We were talking about

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Speaker 3: it and you said, you said, look, I don't want

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Speaker 3: a job in Washington, and you said, all I want

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Speaker 3: is the log in for every computer. And I remember

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Speaker 3: thinking at the time that sounded kind of weird, like

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Speaker 3: I just didn't get it. And I have to say,

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Speaker 3: what's interesting on this? If I would have thought like, okay,

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Speaker 3: how do you reform government? Like sort of the traditional

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Speaker 3: way to think about it is, okay, give me an

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Speaker 3: orc chart, let me sit down with the people who

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Speaker 3: are running agencies. And what you saw immediately is to

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Speaker 3: understand what's really going on, get to the payment systems,

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Speaker 3: get to the computers. Yeah, Like, why is getting to

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Speaker 3: the computers so critical to understanding what's actually happening?

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Speaker 4: Well, the government is run by computers, So you've got

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Speaker 4: essentially several hundred computers that effectively run the government.

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Speaker 2: And if you want to know, did you know that

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Speaker 2: then no?

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Speaker 4: Like, yeah, so when somebody, like even when the president

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Speaker 4: issues an executive order, that's going to go through a

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Speaker 4: whole bunch of people until ultimately it is implemented at

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Speaker 4: a computer somewhere. And if you want to know what

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Speaker 4: the situation is with the accounting and you're trying to

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Speaker 4: reconcile accounting and get rid of waste and fraud, you

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Speaker 4: must be able to analyze the computer databases. Otherwise you

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Speaker 4: can't figure it out because all you're doing is asking

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Speaker 4: a human who will then ask another human, ask another human,

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Speaker 4: and finally usually ask some contractor will ask another contractor

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Speaker 4: to do a query on the computer. Wow, that's how

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Speaker 4: it actually works. So it's many layers d. So the

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Speaker 4: only way to reconcile the databases and get rid of

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Speaker 4: waste and fraud is to to actually look at the

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Speaker 4: computers and see what's going on. So that's what I

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Speaker 4: call you. That's like, that's why when I sort of

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Speaker 4: cryptically referred to reprogramming the matrix. You have to understand

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Speaker 4: what's going on the computers. You have to reconcile the

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Speaker 4: computer databases in order to identify the waste and fraud.

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Speaker 3: I don't know that there was anyone in Congress who understood,

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Speaker 3: certainly myself and do understood the leverage that comes from

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Speaker 3: the computer and the data in particular, that that Congress

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Speaker 3: would think about give me a report on what your

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Speaker 3: expenditures are rather than actually getting into the pipes. And

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Speaker 3: I think that has been fascinating that it's let you

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Speaker 3: uncover a bunch of craft that just nobody knew.

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Speaker 4: Yes, I mean, in order for money to go to

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Speaker 4: evan Acount, it's it's not like we're sending truckloads of

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Speaker 4: cash all over the place where it's a we're wiring money, right,

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Speaker 4: We're sending money through the ACH system or through the

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Speaker 4: Swift system. So in order for money to flow, it's

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Speaker 4: going to flow electronically. So that's that's what you need

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Speaker 4: to look at. You can look at the actual electronic

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Speaker 4: money flows.

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Speaker 3: And Tesla and all your companies, you have accounting and

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Speaker 3: you have every expenditure, you have it coded for what

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Speaker 3: it's going for. Federal government doesn't work that way. They

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Speaker 3: don't code what the money's going for.

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Speaker 2: They do know, but they didn't. They didn't.

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Speaker 3: And like one of the things that that you told me,

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Speaker 3: you said, if any company kept its books the way

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Speaker 3: the federal government does, they'd arrest the officers and put

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Speaker 3: them in jail.

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Speaker 4: Yes, if it was a poly company would be dealisted immediately,

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Speaker 4: it would fail, it's ordered and the officers of the

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Speaker 4: company would be imprisoned. That's the level of enal seasiness

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Speaker 4: in the middle.

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Speaker 1: Unfortunately, it's deliberately or do you think this is in

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Speaker 1: competence again.

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Speaker 4: It's eighty percent. It's eighty percent in competence or in

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Speaker 4: twenty percent malice.

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Speaker 1: So if you look at it, if you look at

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Speaker 1: DOGE now and you look at the government and what

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Speaker 1: you're finding, what percentage have you guys even gotten to

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Speaker 1: and how much of it is mars where you haven't

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Speaker 1: even gotten there yet because there's so much you're finding

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Speaker 1: out here. I mean, how many you seem like a

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Speaker 1: timeline guy when you say, all I want to get

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Speaker 1: in there and to get all these you know, numbers

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Speaker 1: and things. How far are we from the D game

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Speaker 1: where you've seen it all been able to process it

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Speaker 1: all and fix it. I mean are we years away?

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Speaker 1: Months away?

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Speaker 2: Not yours?

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Speaker 4: I mean recentaly confident that we'll be able to get

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Speaker 4: a trillion dollars of waste and APT and that that

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Speaker 4: meaning that it will have we'll have a net savings

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Speaker 4: in f I twenty six, which starts.

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Speaker 2: In October, obviously of a trillion dollars.

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Speaker 4: But Provider, we're allowed to we're allowed to continue, and

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Speaker 4: we're and our progress is not impeded. And we're very

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Speaker 4: public about what we do. Yeah, put it off on

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Speaker 4: the website about how we could be more transparent. Literally,

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Speaker 4: everything action we do, small or large, we put on

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Speaker 4: the dose dot dot com website and we post on

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Speaker 4: the X handle. And when people complain about it I

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Speaker 4: and they say, oh, you're doing something on costumes, I'm like, well,

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Speaker 4: which of these costs in the daylight.

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Speaker 3: Everyone knows exactly what you're doing.

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Speaker 2: Extreme transparent.

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Speaker 4: Yeah, I don't think it's anything's been this transparent ever.

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Speaker 3: So five years ago you were a hero to the left. Cool,

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Speaker 3: you had electric cars, you had space, and in five

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Speaker 3: years you've.

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Speaker 4: Got to go to a party in Hollywood and not

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Speaker 4: get dirty looks.

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Speaker 2: Yeah. In fact, yeah, and now you might not even

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Speaker 2: get invited. I think is invited, but I don't know

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Speaker 2: if I should go.

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Speaker 3: And I don't think it's an exaggeration to say, today,

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Speaker 3: after Donald Trump, the left hates you more than a

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Speaker 3: person on earth.

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Speaker 4: Yes, I appear to be number two. I mean, if

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Speaker 4: you're judged by the various signs.

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Speaker 3: They estrangements, it's Trump de arrangement syndrome and Elon derangement syndrome.

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Speaker 3: How is that for you? That's a little bit of

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Speaker 3: whiplash of going from being like mister cool to the

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Speaker 3: devil incarnate in just a couple of years. Is that

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Speaker 3: is that kind of weird to experience that transformation?

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Speaker 2: Yes?

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Speaker 3: Why do they hate you so much?

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Speaker 4: Well, because we're we're clearly over the target if those

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Speaker 4: was ineffective, if we were not actually getting rid of

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Speaker 4: a bunch of waste and forward and a bunch of

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Speaker 4: that Ford. I mean, the Ford reason we're seeing is

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Speaker 4: overwhelmingly on the on the left. I mean, it's not

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Speaker 4: zero on the right, but these NGOs are almost all

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Speaker 4: left wing NGOs that are being funded for Apple. So

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Speaker 4: they hate me because Dog is being effective and Doge

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Speaker 4: is getting rid of a lot of waste for that

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Speaker 4: they were that people and left were taking advantage of that.

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Speaker 4: That's that's that's what it comes out to you. And

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Speaker 4: and the single biggest thing that they're that they're worried

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Speaker 4: about is that Dog is going is going to turn

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Speaker 4: off fraudulent payments of entitlements. I mean everything from Social Security, medicare,

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Speaker 4: uh you know, unemployment, disability, smallpers administ regustration loans, turn

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Speaker 4: them off to illegals. This is that clus to the matter.

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Speaker 4: Ye okay, this is this is the this is the

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Speaker 4: thing that why they really hit my guts, want me

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Speaker 4: to die.

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Speaker 3: And do you think that's billions hundreds of billions? What

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Speaker 3: do you think the scale is of that?

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Speaker 4: I think across the country, it's it's in the it's

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Speaker 4: well noth of one hundred billion, maybe two hundred billion,

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Speaker 4: so uh bye. By using entitlements fraud, the Democrats have

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Speaker 4: been able to attract and retain vast numbers of illegal

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Speaker 4: immigrants and buy voters, and buy voters exactly the It

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Speaker 4: basically bring in ten twenty million people who are beholden

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Speaker 4: to the Democrats for government handouts and will vote overwhelming

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Speaker 4: the Democrat as has been demonstrated in California. This is

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Speaker 4: it's an election strategy. Yes, it's powered, yes, and it

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Speaker 4: doesn't take much to turn the swing states blue. I

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Speaker 4: mean off in a swing state yet be won by

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Speaker 4: ten twenty thousand votes. Sure so if the DAMS can

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Speaker 4: bring in two hundred thousand illegals and over time get

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Speaker 4: them legalized, not counting any cheating that takes place, because

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Speaker 4: there is some cheating, but even without cheating, if you bake,

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Speaker 4: if you if you bring in illegals that are ten

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Speaker 4: x the voted differential in a swing state, it will

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Speaker 4: no longer be a swing state, right, and the DAMS

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Speaker 4: will win all the swing states just amount of time,

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Speaker 4: and America will be permanent deep blue socialist state. At

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Speaker 4: the House, the Senate, YEP, the Presidency, and the Supreme.

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Speaker 2: Court will all go hardcore down.

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Speaker 4: They will then further cement that by bringing even more

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Speaker 4: aliens so you can't vote your way out of it.

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Speaker 4: Their objemin is to make one party socialist state, and

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Speaker 4: it will be much worse than California. Because at least

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Speaker 4: California is mitigated by the fact that someone can leave California.

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Speaker 3: You can go to Texas.

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Speaker 4: Yeah, exactly, So you're going to make everywhere California but worse.

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Speaker 3: By the way, the middle of the pandemic, I spent

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Speaker 3: forty five minutes on the phone with Elon. He was

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Speaker 3: still in California. I was walking my dog's snowflake and

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Speaker 3: trying to convince you come to Texas. The commis in

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Speaker 3: California can't stand you. We love you, we want you here.

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Speaker 3: And you didn't quite go then, but you went not

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Speaker 3: that long afterwards.

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Speaker 4: I mean, the COVID actions almost killed Tesla because they

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Speaker 4: let every other auto plant in the country was allowed

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Speaker 4: to open, but ours, which was.

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Speaker 2: In California, was not a lot of open.

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Speaker 3: Wow wow.

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Speaker 2: So they almost killed Tesla.

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Speaker 3: So as a personal matter, do you ever regret it?

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Speaker 3: Like five years ago you go to the Oscars and

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Speaker 3: were mister cool, and now you're you've got death threats

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Speaker 3: every day, Like do you well?

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Speaker 2: These days the oscars are boring. I wouldn't want to go.

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Speaker 3: God bless the movies they nominate no one on earth

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Speaker 3: has ever seen, Like could they actually nominate a movie

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Speaker 3: that human beings go watch.

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Speaker 4: I mean, how many great movies have come out in

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Speaker 4: the last several years. Very few, depressingly few, Yeah, very few.

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Speaker 4: Last Oscars came and went I didn't watch it.

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Speaker 2: There's nothing to see.

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Speaker 3: I was sad that Gene Hackman just passed away because

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Speaker 3: Unforgiven was spectacular. But that was a long time ago

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Speaker 3: when Forgiven came out.

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Speaker 1: You've mentioned today here and before about the possibility of

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Speaker 1: someone wanting to take you out, dealing with the death threats.

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Speaker 2: We see, it's not in my imagination. You can just

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Speaker 2: look on social media. Yeah, but like is it because

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Speaker 2: very clear? Yeah.

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Speaker 3: And look, I'm I'm very familiar with.

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Speaker 4: And they've got science, the people with science and demonstrations

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Speaker 4: saying that I need to die.

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Speaker 3: Do you think are these just whack jobs or do

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Speaker 3: you think.

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Speaker 2: There are hopefully foreign sane people.

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Speaker 3: Do you think there are foreign entities behind this? Do

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Speaker 3: you think they're domestic entities behind the threats? And also

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Speaker 3: the attacks to Twitter are not Twitter but Tesla. I mean,

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Speaker 3: you know you're getting Tesla's charging stations lit on fire.

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Speaker 3: Do you think that's organized and paid for?

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Speaker 4: Yes, at least some of it is organized and paid

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Speaker 4: for I think by domestic you know, basically left wing

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Speaker 4: organizations in America funded by left wing billionaires. Essentially, is

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Speaker 4: it like Act Blue or what Act Blue.

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Speaker 2: Is one of them?

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Speaker 4: You know Arabella, you know the classic it's funded by

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Speaker 4: the you know, the the blue basically the left wing

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Speaker 4: ANDNGO cabal.

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Speaker 1: How big of a threads is to like what you

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Speaker 1: build at Tesla? I mean, I remember when Tesla's came out,

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Speaker 1: it was people that they didn't want to have gas cars.

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Speaker 1: A lot of it was environmental reasons. I jokingly said,

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Speaker 1: I was like, I'm a Texas guy, I'm always going

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Speaker 1: to have something that burns gas. My kids now, all

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Speaker 1: three of my boys think that that Tesla's are awesome.

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Speaker 1: The cyber truck is the car they want their dad

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Speaker 1: to buy, which I laugh because I never could have

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Speaker 1: imagined that five years ago. And now I'm looking at

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Speaker 1: We're worth.

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Speaker 3: The White House in the President's tesla'sparking. Yeah, I mean,

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Speaker 3: which is the fullest stand. But I mean, you've changed

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Speaker 3: a generation.

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Speaker 1: When you look at my kids are six and eight

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Speaker 1: and they're going, Dad buy a cyber truck, and I'm

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Speaker 1: considering it. That's a that's a full circle in a

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Speaker 1: weird way.

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Speaker 4: Yeah, Well, I do have the story that the most

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Speaker 4: errantating outcome is the most likely. So yeah, it seems

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Speaker 4: often to be true. What what twist or two of fate? Well,

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Speaker 4: I would the highest ratings if this was if we're

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Speaker 4: a TV show, what crystal turn of fate would generate

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Speaker 4: the highest ratings? That there's a good chance that happens.

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Speaker 3: Well, I will say if if Act Blue and Arabella.

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Speaker 2: Network Blue is a huge scam, next level, do.

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Speaker 3: You think it's foreign money, Chinese money? Where do you

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Speaker 3: think the money in Act Blue is coming from? And

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Speaker 3: how do you figure that out?

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Speaker 4: Well, it's not coming from the from a whole bunch

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Speaker 4: of from a ground swell of public support, because when

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Speaker 4: individual donors looked at and Act Blue, they avntually turned

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Speaker 4: out to be like diehard Republicans. Right, people have never

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Speaker 4: given money in their life. So you're going to track

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Speaker 4: down a bunch of these where it says, oh, I

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Speaker 4: gave sixteen thousand dollars and they're like, I didn't give

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Speaker 4: sixteen thousand dollars.

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Speaker 2: We're talking about.

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Speaker 4: Well, if those Fokan friends of mine, if I found

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Speaker 4: themselves on the Act Blue list, they like.

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Speaker 3: So that's if it can actually be shown that they

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Speaker 3: are funding firebombing of Tesla charging stations, that's objectively a

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Speaker 3: criminal act. That that is fun terrorist activity. And the

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Speaker 3: statutes make clear that an incendiary device qualify, so that

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Speaker 3: down is a terrorist activity. Yeah, let me ask AI.

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Speaker 3: In ten years, how is life going to be different

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Speaker 3: because of AI for just a normal person.

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Speaker 2: Well, ten years is a long time.

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00:22:20,400 --> 00:22:24,000
Speaker 4: In ten years, probably AI could do anything better than

442
00:22:24,040 --> 00:22:25,600
Speaker 4: a human can cognitively.

443
00:22:26,080 --> 00:22:26,760
Speaker 2: Probably almost.

444
00:22:27,200 --> 00:22:29,760
Speaker 4: I think in ten years, based on the cart rate

445
00:22:29,800 --> 00:22:32,920
Speaker 4: of improvement, AI will be smatter than the smasht human.

446
00:22:33,880 --> 00:22:34,120
Speaker 2: Yeah.

447
00:22:34,200 --> 00:22:38,600
Speaker 4: Yeah, there will also be a massive number of robots,

448
00:22:39,040 --> 00:22:40,359
Speaker 4: so humanoid robots.

449
00:22:40,720 --> 00:22:42,119
Speaker 3: By the way, I got to ask, how come your

450
00:22:42,160 --> 00:22:44,879
Speaker 3: robots look so much like the creepy robots for my robot?

451
00:22:45,760 --> 00:22:47,280
Speaker 3: Was that intentional or just.

452
00:22:49,160 --> 00:22:50,920
Speaker 1: I was hoping he's gonna say, yeah, just to mess

453
00:22:50,960 --> 00:22:51,159
Speaker 1: with you.

454
00:22:52,920 --> 00:22:56,080
Speaker 4: It's not meant to look like any any prior robot.

455
00:22:56,840 --> 00:23:00,400
Speaker 4: And we'll iterate the design and you'll be able to

456
00:23:00,600 --> 00:23:03,400
Speaker 4: have a lot of the robot parts are cosmetic. You'll

457
00:23:03,440 --> 00:23:06,879
Speaker 4: be able to switch out the kind of snap on

458
00:23:07,119 --> 00:23:09,360
Speaker 4: cosmetic parts of the robot make it look like something

459
00:23:09,359 --> 00:23:15,919
Speaker 4: else if you're lying. So there'll be ultimately billions of

460
00:23:16,000 --> 00:23:17,320
Speaker 4: humanoid robots.

461
00:23:17,600 --> 00:23:21,120
Speaker 2: All costs will be self driving in ten years.

462
00:23:21,760 --> 00:23:25,640
Speaker 4: In ten years, probably ninety percent of miles driven will

463
00:23:25,680 --> 00:23:26,880
Speaker 4: be autonomous.

464
00:23:26,960 --> 00:23:28,439
Speaker 3: Huh wow that fast.

465
00:23:29,560 --> 00:23:30,600
Speaker 2: Yeah.

466
00:23:30,840 --> 00:23:34,320
Speaker 4: In five years, probably fifty percent of all miles will

467
00:23:34,440 --> 00:23:35,520
Speaker 4: driven will be autonomous.

468
00:23:35,520 --> 00:23:38,400
Speaker 3: Now, if AI will be smarter than any person, how

469
00:23:38,400 --> 00:23:42,680
Speaker 3: many jobs go away because of that? And what do

470
00:23:42,760 --> 00:23:45,160
Speaker 3: people do if you've got billions of people they're losing

471
00:23:45,160 --> 00:23:47,959
Speaker 3: their jobs like that a lot of people are understandably

472
00:23:48,000 --> 00:23:48,880
Speaker 3: freaked out about that.

473
00:23:49,760 --> 00:23:55,720
Speaker 2: Well, good goods and services will become close to free,

474
00:23:56,600 --> 00:23:59,399
Speaker 2: so as long as though people will be wanting in

475
00:23:59,480 --> 00:24:04,399
Speaker 2: terms of goods services.

476
00:24:02,720 --> 00:24:03,480
Speaker 3: So why is that?

477
00:24:03,480 --> 00:24:03,960
Speaker 2: What?

478
00:24:04,000 --> 00:24:06,880
Speaker 3: Why are goods and services free in an AI world

479
00:24:07,440 --> 00:24:09,040
Speaker 3: or close to free?

480
00:24:09,280 --> 00:24:12,200
Speaker 4: Well, you have I don't know, pull it tens of

481
00:24:12,200 --> 00:24:16,320
Speaker 4: billions of robots that they will they will make you

482
00:24:16,400 --> 00:24:21,880
Speaker 4: anything or provide any service you want, or basically next

483
00:24:21,880 --> 00:24:27,520
Speaker 4: to nothing. It's it's not that people will be will

484
00:24:27,560 --> 00:24:29,040
Speaker 4: have a lower standard of living. They will have actually

485
00:24:29,119 --> 00:24:35,679
Speaker 4: much higher standard of living. The challenge will be fulfillment.

486
00:24:35,720 --> 00:24:39,040
Speaker 4: How do you drive fulfillment and meeting in life?

487
00:24:39,359 --> 00:24:44,760
Speaker 3: Is skynet real, like you get the apocalyptic visions of AI.

488
00:24:46,080 --> 00:24:52,280
Speaker 3: How real is the prospect of killer robots annihilating humanity?

489
00:24:52,800 --> 00:24:57,600
Speaker 2: Likely? Maybe ten percent on what timeframe perhaptally ten years

490
00:24:58,520 --> 00:24:59,080
Speaker 2: so soon?

491
00:24:59,520 --> 00:25:02,200
Speaker 3: Like you, you see a world where that's possible.

492
00:25:03,520 --> 00:25:05,000
Speaker 4: Yeah, But I mean you can look at it like

493
00:25:05,040 --> 00:25:09,639
Speaker 4: the glasses eighty ninety percent full, meaning like eighty percent

494
00:25:09,920 --> 00:25:13,960
Speaker 4: likely will have extreme prosperity for all.

495
00:25:15,119 --> 00:25:17,560
Speaker 3: Now, I guess my view we're in a race to

496
00:25:17,960 --> 00:25:21,240
Speaker 3: win AI. We're in a race with China, and my

497
00:25:21,359 --> 00:25:23,160
Speaker 3: view is if they're going to be killer robots, I'd

498
00:25:23,240 --> 00:25:27,920
Speaker 3: rather they be American killer robots than Chinese. How likely

499
00:25:28,640 --> 00:25:30,960
Speaker 3: are we winning right now? Is America winning right now?

500
00:25:31,000 --> 00:25:33,280
Speaker 3: And how likely is America to win the race for

501
00:25:33,359 --> 00:25:35,480
Speaker 3: AI visav China or anyone else?

502
00:25:36,200 --> 00:25:38,440
Speaker 4: Well the next few years. I think America is likely

503
00:25:38,480 --> 00:25:41,360
Speaker 4: to win. Then it will be a function of who

504
00:25:41,359 --> 00:25:46,560
Speaker 4: controls the AI chip fabrication, the factories that make the

505
00:25:46,560 --> 00:25:50,000
Speaker 4: AI chiefs, who controls them if they are controlled, If

506
00:25:50,000 --> 00:25:52,200
Speaker 4: more of them will controlled by China than China will win.

507
00:25:53,119 --> 00:25:55,760
Speaker 3: More of the factories that are making the AI chips.

508
00:25:56,000 --> 00:25:57,199
Speaker 3: You think that will determine it?

509
00:25:57,600 --> 00:25:57,919
Speaker 2: Yes?

510
00:25:58,720 --> 00:26:01,040
Speaker 3: And how are we doing just China on that front.

511
00:26:02,080 --> 00:26:07,800
Speaker 2: Well, right now, almost all the advanced AI chip factories

512
00:26:07,840 --> 00:26:12,160
Speaker 2: they call them fabs, are in Taiwan.

513
00:26:12,359 --> 00:26:14,840
Speaker 3: And what if China invades one miles away from Yeah,

514
00:26:15,160 --> 00:26:20,200
Speaker 3: what happens if China? Yea, if China invades Taiwan, what happens.

515
00:26:19,320 --> 00:26:19,879
Speaker 2: To the world.

516
00:26:20,960 --> 00:26:24,639
Speaker 4: Well, if they were to invade in the NATOM, the

517
00:26:24,680 --> 00:26:28,320
Speaker 4: world would be cut off from advanced AI chips. And

518
00:26:28,640 --> 00:26:31,440
Speaker 4: currently one percent of advanced the AI chips all made

519
00:26:31,440 --> 00:26:31,920
Speaker 4: in Taiwan.

520
00:26:32,240 --> 00:26:34,159
Speaker 1: How fast can we put that online in America? How

521
00:26:34,200 --> 00:26:36,840
Speaker 1: important is that for national security?

522
00:26:36,960 --> 00:26:40,000
Speaker 2: I think it's essential for national security, and we're not

523
00:26:40,040 --> 00:26:40,600
Speaker 2: doing enough.

524
00:26:41,440 --> 00:26:44,399
Speaker 3: You're fifty three years old. I'm one hundred and eighteen

525
00:26:44,480 --> 00:26:46,320
Speaker 3: days older than you. By what the hell have I

526
00:26:46,400 --> 00:26:47,359
Speaker 3: done in my life? I know?

527
00:26:47,480 --> 00:26:47,720
Speaker 2: Right?

528
00:26:48,520 --> 00:26:49,680
Speaker 3: Fifty three years old?

529
00:26:49,920 --> 00:26:54,439
Speaker 2: Pretty well? Well, so seventy one was a great.

530
00:26:54,359 --> 00:26:57,600
Speaker 3: Year, and I was December seventy because I was just

531
00:26:57,600 --> 00:27:00,240
Speaker 3: just right before you were the summer of seventy one.

532
00:27:01,520 --> 00:27:03,440
Speaker 2: I was born sixty nine days after four twenty.

533
00:27:04,359 --> 00:27:10,960
Speaker 3: Wow, I did ask Ben, this is true. Look, I

534
00:27:11,000 --> 00:27:12,919
Speaker 3: did ask Ben. Should I show up and pull up

535
00:27:12,960 --> 00:27:15,640
Speaker 3: a joint and say, can we beat Rogan's views? But

536
00:27:15,640 --> 00:27:18,880
Speaker 3: but I was pretty sure it might cause a scandal

537
00:27:18,880 --> 00:27:20,640
Speaker 3: if if we spent a podcast.

538
00:27:21,080 --> 00:27:22,800
Speaker 2: It just turned out to be like a Chocolates of garden.

539
00:27:23,240 --> 00:27:27,879
Speaker 3: Yeah, let me ask you if if today was your

540
00:27:27,960 --> 00:27:28,760
Speaker 3: last day on Earth?

541
00:27:29,000 --> 00:27:30,240
Speaker 2: Yeah, what what?

542
00:27:30,440 --> 00:27:32,440
Speaker 3: I'm not suggesting it's going to be. But if it were,

543
00:27:32,440 --> 00:27:34,200
Speaker 3: what do you think your biggest legacy would be if everything

544
00:27:34,200 --> 00:27:37,000
Speaker 3: you've done one hundred years from now? What do you

545
00:27:37,040 --> 00:27:40,320
Speaker 3: think people would remember if if if if it were

546
00:27:40,480 --> 00:27:41,680
Speaker 3: zero to today.

547
00:27:41,480 --> 00:27:43,080
Speaker 1: And were you ever going to space.

548
00:27:44,640 --> 00:27:47,560
Speaker 4: In the in the distant future one hundred or one

549
00:27:47,560 --> 00:27:51,560
Speaker 4: thousand years ago, if SpaceX got humans to Mars, that's

550
00:27:51,640 --> 00:27:53,159
Speaker 4: what they would remember me for.

551
00:27:54,440 --> 00:27:57,280
Speaker 3: All Right, final set of questions. Who's the smartest guy

552
00:27:57,320 --> 00:28:01,040
Speaker 3: you've ever met? You hang out with brilliant people, like

553
00:28:01,040 --> 00:28:03,480
Speaker 3: like when you look at what's a CEO? You look

554
00:28:03,520 --> 00:28:07,800
Speaker 3: at other than yourself, what CEO? Do you say? Damn,

555
00:28:07,920 --> 00:28:08,640
Speaker 3: that guy's good.

556
00:28:10,320 --> 00:28:16,119
Speaker 4: Larry Elson's very smart. So I say, Larry Elson's one

557
00:28:16,119 --> 00:28:21,320
Speaker 4: of the smartest people. You know, Larry Page. I mean,

558
00:28:21,359 --> 00:28:22,160
Speaker 4: there are a lot of people.

559
00:28:21,960 --> 00:28:22,480
Speaker 2: That are very smart.

560
00:28:22,480 --> 00:28:25,000
Speaker 4: It's hard to say, like, you know, I think something

561
00:28:25,000 --> 00:28:29,359
Speaker 4: to be smart is as smart as so you know,

562
00:28:29,400 --> 00:28:37,439
Speaker 4: what have they done that is difficult and significant. You know,

563
00:28:37,560 --> 00:28:41,040
Speaker 4: Jeff Bezos has done a lot of difficult and significant things.

564
00:28:43,640 --> 00:28:46,120
Speaker 4: I mean, there are a lot of smart humans. I

565
00:28:46,200 --> 00:28:48,479
Speaker 4: call them smart for smart for a human, A lot

566
00:28:48,520 --> 00:28:50,520
Speaker 4: of people who are in the smart for a human category.

567
00:28:51,080 --> 00:28:54,760
Speaker 3: All right, final lightning round, Star Wars or Star Trek.

568
00:28:55,960 --> 00:28:58,080
Speaker 4: The first movie I said one to theater was Star Wars,

569
00:28:58,120 --> 00:28:59,800
Speaker 4: so I think it had a profound effect on me.

570
00:29:00,920 --> 00:29:01,760
Speaker 2: I'm six years old.

571
00:29:01,920 --> 00:29:06,720
Speaker 4: I think, imagin best first movie you ever see in

572
00:29:06,800 --> 00:29:07,880
Speaker 4: a theater a Star Wars.

573
00:29:07,920 --> 00:29:08,800
Speaker 2: It's going to blow your mind.

574
00:29:08,880 --> 00:29:09,960
Speaker 3: Best Star Wars.

575
00:29:09,640 --> 00:29:13,200
Speaker 2: Movie Empires Trucks Back.

576
00:29:13,400 --> 00:29:16,120
Speaker 3: The only objectively right answer. I stood and learned in

577
00:29:16,160 --> 00:29:18,280
Speaker 3: three hours with my dad to see it on opening day.

578
00:29:19,120 --> 00:29:22,120
Speaker 2: Kirk or Picard, I like them both.

579
00:29:22,160 --> 00:29:26,920
Speaker 3: For Kirk again, objectively right answer. By the way, James T.

580
00:29:27,040 --> 00:29:30,080
Speaker 3: Kirk is a Republican and Picard is a Democrat, and

581
00:29:30,280 --> 00:29:33,800
Speaker 3: the left gets very mad when I say that best

582
00:29:33,800 --> 00:29:35,080
Speaker 3: Star Trek movie.

583
00:29:35,920 --> 00:29:37,960
Speaker 2: I mean the original, the first Star Trek movie.

584
00:29:38,680 --> 00:29:45,040
Speaker 4: That's okay, most of both of both.

585
00:29:45,080 --> 00:29:47,400
Speaker 2: Wrath of cons were pretty good, but yeah, the original

586
00:29:47,400 --> 00:29:48,160
Speaker 2: Wrath of kN.

587
00:29:48,840 --> 00:29:53,920
Speaker 3: Ricardo Montaban Revenge is a dish best served cold. It

588
00:29:54,000 --> 00:29:58,880
Speaker 3: is very cold in space, although I will say Wrathakon

589
00:29:59,000 --> 00:30:01,320
Speaker 3: is objectively the right a answer. But but four as

590
00:30:01,360 --> 00:30:04,320
Speaker 3: a sleeper when they go back to San Francisco and

591
00:30:04,320 --> 00:30:06,640
Speaker 3: and and go find the whales and and you know,

592
00:30:06,720 --> 00:30:09,880
Speaker 3: Scotty picks up picks up the mouth and talks to

593
00:30:09,920 --> 00:30:13,760
Speaker 3: it and goes a keyboard. How quaint that's a sleeper?

594
00:30:13,800 --> 00:30:16,640
Speaker 3: All right? Last question? Did Han shoot first?

595
00:30:18,480 --> 00:30:21,640
Speaker 2: It seemed like he shot second. This is verdict.

596
00:30:21,720 --> 00:30:24,200
Speaker 3: And by the way, I apologize Ben so Ben was

597
00:30:24,240 --> 00:30:26,600
Speaker 3: a jock and played tennis at ole miss and so

598
00:30:26,600 --> 00:30:30,120
Speaker 3: so occasionally when when we geek out a little watching y'all.

599
00:30:29,960 --> 00:30:33,880
Speaker 4: Geek out over there is still on the question you

600
00:30:33,960 --> 00:30:36,760
Speaker 4: miss the alien missters flash shot? So why do you

601
00:30:36,760 --> 00:30:38,720
Speaker 4: miss his flaster shot? Must have been because he got

602
00:30:38,880 --> 00:30:41,959
Speaker 4: shot first. He's missing a point blank bastle shot. If

603
00:30:41,960 --> 00:30:43,680
Speaker 4: the less they got knocked off kids.

604
00:30:43,680 --> 00:30:47,520
Speaker 3: But it's a question of real which is is Han

605
00:30:47,680 --> 00:30:50,520
Speaker 3: solo simply a hero or an anti hero? And and

606
00:30:50,600 --> 00:30:53,360
Speaker 3: so I'm in the Han shot first category. I think

607
00:30:53,360 --> 00:30:54,880
Speaker 3: I don't like sanitized stories.

608
00:30:54,960 --> 00:30:57,400
Speaker 4: You would have shot first because they why why would

609
00:30:57,400 --> 00:30:59,480
Speaker 4: the alien miss a point blank range?

610
00:30:59,800 --> 00:31:00,000
Speaker 2: Are you?

611
00:31:00,000 --> 00:31:01,640
Speaker 1: Are you ever going to go to outer space?

612
00:31:01,720 --> 00:31:03,000
Speaker 3: Is that thing in your life goals?

613
00:31:03,240 --> 00:31:04,640
Speaker 4: Yeah, I'd like to go to Mars at some point.

614
00:31:04,760 --> 00:31:08,240
Speaker 4: And people have said do I want to die maz

615
00:31:09,040 --> 00:31:10,680
Speaker 4: and I say yes, just not an impact.

616
00:31:11,520 --> 00:31:14,600
Speaker 3: Now, that's a very good answer. The astronauts on the

617
00:31:14,600 --> 00:31:19,320
Speaker 3: space station, are they political prisoners? Some of them are

618
00:31:20,400 --> 00:31:22,560
Speaker 3: because because you could have given them a ride back,

619
00:31:22,640 --> 00:31:26,719
Speaker 3: and Joe Biden said no, purely for politics.

620
00:31:27,040 --> 00:31:30,480
Speaker 4: Yeah, I mean, you know, there's been some debate about

621
00:31:30,480 --> 00:31:32,840
Speaker 4: this online. But the thing is that it was very

622
00:31:32,880 --> 00:31:35,880
Speaker 4: a very high level of decision, so it wasn't really

623
00:31:35,920 --> 00:31:39,880
Speaker 4: even an acid decision. It was just that the Biden

624
00:31:39,920 --> 00:31:42,600
Speaker 4: White House did not want to have someone who is

625
00:31:42,920 --> 00:31:48,000
Speaker 4: pro Trump rescuing Askroot's rife before the election, so they

626
00:31:48,000 --> 00:31:48,360
Speaker 4: pushed it.

627
00:31:48,480 --> 00:31:50,080
Speaker 3: Well, if you're one of those astronauts, you gotta be

628
00:31:50,080 --> 00:31:51,280
Speaker 3: pretty pissed off about that.

629
00:31:52,280 --> 00:31:58,280
Speaker 4: Well, if they're a Democrat, yes, a Democrat, like, everything's fine,

630
00:31:58,360 --> 00:32:00,680
Speaker 4: fair enough. So I think one of them is a

631
00:32:00,680 --> 00:32:03,360
Speaker 4: republicable incident. So it depends on which one you're asked.

632
00:32:03,640 --> 00:32:06,400
Speaker 3: Well, thank you elon this. This was awesome. And let

633
00:32:06,400 --> 00:32:09,440
Speaker 3: me say and by the way, I put out on

634
00:32:09,760 --> 00:32:12,000
Speaker 3: X the day before yesterday, if you were having a

635
00:32:12,000 --> 00:32:14,000
Speaker 3: beer with Elon and could ask him anything, what would

636
00:32:14,040 --> 00:32:17,360
Speaker 3: you ask? And got lots of responses. The most common

637
00:32:17,400 --> 00:32:21,480
Speaker 3: response people said is is say thank you. Look, Texans

638
00:32:21,480 --> 00:32:24,280
Speaker 3: and the American people appreciate what you're doing. You don't

639
00:32:24,320 --> 00:32:26,520
Speaker 3: have to put up with this bs, and you're doing it.

640
00:32:26,560 --> 00:32:29,160
Speaker 3: I'm grateful. You're making a hell of a difference for

641
00:32:29,200 --> 00:32:32,360
Speaker 3: this country. I appreciate you, and the Americans appreciate you.

642
00:32:32,640 --> 00:32:34,920
Speaker 4: Yeah, it's essential for the future of civilization. Otherwise I

643
00:32:34,920 --> 00:32:36,640
Speaker 4: wouldn't be doing it. Yes, it's so like I want

644
00:32:36,680 --> 00:32:37,640
Speaker 4: to get death threats.

645
00:32:37,440 --> 00:32:38,000
Speaker 2: You know. No.

646
00:32:38,480 --> 00:32:41,080
Speaker 1: All right now, Part two of this interview with Ion

647
00:32:41,160 --> 00:32:43,920
Speaker 1: Mosk and some bigger breaking news that we're going to

648
00:32:44,000 --> 00:32:47,240
Speaker 1: have for you about corruption the government will hit on

649
00:32:48,000 --> 00:32:51,240
Speaker 1: Wednesday morning, So make sure you get that subscribe.

650
00:32:50,680 --> 00:32:52,800
Speaker 3: Or auto Dowllo button right.

651
00:32:52,600 --> 00:32:55,800
Speaker 1: Now, and again, please help this show go viral so

652
00:32:55,840 --> 00:32:59,120
Speaker 1: that we can continue to expose government waste wherever you're

653
00:32:59,160 --> 00:33:02,720
Speaker 1: on social media. Hit that little forward button and post

654
00:33:02,880 --> 00:33:05,840
Speaker 1: this episode on social media and the Center and I

655
00:33:05,880 --> 00:33:08,440
Speaker 1: will see you back here with Elon Moss Part two

656
00:33:08,600 --> 00:33:09,320
Speaker 1: Wednesday morning,