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Speaker 1: Hey, everybody, Robert Evans here again. As I stated last time,

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Speaker 1: this episode ran very long, so we split into two parts. UM.

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Speaker 1: I'm going to just dive into the episode with Jake

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Speaker 1: and I right now, So thanks for listening watching if

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Speaker 1: you can see sound. Um. In any case, here's more bezos.

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Speaker 1: Jeff had been a demanding taskmaster to his employees up

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Speaker 1: to this point, but he also sounds like he was

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Speaker 1: a reasonably well liked boss that complaints you here later

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Speaker 1: about him. He's very strict, he's very like dedicated. He

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Speaker 1: wants a lot of work out of you. UM. I

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Speaker 1: haven't heard anything like allegations that he was abusive at

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Speaker 1: this point, and that's because I think he's not really

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Speaker 1: in charge. He's not running the company. He's got people

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Speaker 1: above him, and I think that tempers some of what

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Speaker 1: will later become his worst attributes as a boss. For

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Speaker 1: some time, Jeff had been talking out different business ideas

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Speaker 1: with his mentor, David Shaw. Many of these ideas were

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Speaker 1: financed focused, but one of the ideas they would talk

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Speaker 1: about was what they call the Everything Store. Now. The

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Speaker 1: Internet was young back then, and it was still very small,

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Speaker 1: but both men were enough future focused to understand its

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Speaker 1: potential and the fact that an online company that could

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Speaker 1: sell products directly from manufacturer to consumer could save a

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Speaker 1: fortune on store space and among other things and undercut

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Speaker 1: retail chains. This was not a revolutionary idea. I'm sure

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Speaker 1: a number of people realized this was going to happen

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Speaker 1: at some point, but Jeff and David are talking about

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Speaker 1: it very early on, and Jeff did come up with

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Speaker 1: one innovative plan for how this store would work. His

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Speaker 1: idea was that it should be driven by customer reviews.

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Speaker 1: Any customers should be able to leave a review, UM,

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Speaker 1: and you know, all of those reviews should be collected,

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Speaker 1: and you should get people buying, should be like shown

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Speaker 1: products based on what which stuff is best reviewed? UM. Now,

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Speaker 1: this had never been done on a large scale. That's

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Speaker 1: how the whole internet works now. But Jeff is really

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Speaker 1: like kind of the first person who commits to doing

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Speaker 1: this anywhere in internet commerce. UM. And that's really when

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Speaker 1: you're looking kind of what set Amazon initially apart from

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Speaker 1: other attempts. That was a big part of it. UM.

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Speaker 1: For a while, the Everything Store was a fun thought

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Speaker 1: experiment for Shaw, but Bezos grew obsessed with it in

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Speaker 1: his free time, he would read analyzes of the growth

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Speaker 1: of the early Internet, and in nine he came across

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Speaker 1: information that web activity had surged by two hundred and

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Speaker 1: thirty thousand percent in the last year. So he thought

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Speaker 1: quite accurately that someone could make a shipload of money

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Speaker 1: on the Internet with a growth percentage like that. So

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Speaker 1: he decides to quit his high paying job because he's

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Speaker 1: already very success He could have stayed at D. E.

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Speaker 1: Shaw for the rest of his career and almost certainly

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Speaker 1: would have been a multimillionaire. You know, UM would have

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Speaker 1: been a very successful career, but he's he wants more

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Speaker 1: than that, so he quits his job. Um kind of

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Speaker 1: baffles his mentor, and he and his new wife, Mackenzie

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Speaker 1: Flight of Texas, grab a ship car from some family

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Speaker 1: members and drive to California. Mackenzie drives while Jeff spends

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Speaker 1: the whole time like putting together spreadsheets and reading different

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Speaker 1: sort of business laws and figuring out how he's going

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Speaker 1: to make this this company he wants to form work Um.

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Speaker 1: One of the things he finds out on his drive

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Speaker 1: is that, due to a nineteen two Supreme Court case,

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Speaker 1: merchants did not have to collect sales tax and states

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Speaker 1: they didn't operate in. And this is kind of the

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Speaker 1: loophole that makes early Amazon dot Com possible, the fact

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Speaker 1: that like again when it because a big part of

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Speaker 1: it is that they can really undercut prices. Part of

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Speaker 1: that is we're not paying for a brick and mortar store,

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Speaker 1: so we don't have to pay for like employees to

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Speaker 1: stock ship, we don't have to play pay for storage space.

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Speaker 1: We can sell direct from the manufacturer. But part of

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Speaker 1: it is due that is because if you're an online

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Speaker 1: retailer in this period, you don't have to collect sales

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Speaker 1: tax from any state but the one year in So

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Speaker 1: if people who live in the state that your business

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Speaker 1: is operated in buy from you, they have to pay

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Speaker 1: sales tax, but nobody else does, which again means you

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Speaker 1: can undercut everybody else. So Jeff had initially picked California

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Speaker 1: as the place to launch Amazon dot Com, but when

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Speaker 1: he learns about this law, he moves to Washington State instead.

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Speaker 1: Um and he picks Washington because number one, no income

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Speaker 1: tax in Washington. I think that has an impact on it.

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Speaker 1: Number two, and I think the main reason he picks

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Speaker 1: it is because it has really good infrastructure. Microsoft is

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Speaker 1: based near Seattle, so it has the kind of infrastructure

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Speaker 1: you need to create a tech company. But almost no

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Speaker 1: one lives in Washington. The whole Pacific Northwest is very

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Speaker 1: like sparsely populated, so it meant that like the least

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Speaker 1: number of customers possible would have to pay sales tax

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Speaker 1: for Amazon products, and no one in the other forty

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Speaker 1: nine states has to pay. The initial plan was to

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Speaker 1: sell books, because virtually all books came from one or

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Speaker 1: two distribution companies who already had good distribution infrastructure, and

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Speaker 1: because books are comparatively simple to ship and store. The

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Speaker 1: plan from the beginning, but from Jeff, was to make

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Speaker 1: money selling a single product online and then branch out

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Speaker 1: eventually to selling everything. The first name for what would

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Speaker 1: be called Amazon was very stupid. Jeff wanted to call

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Speaker 1: it Cadabra Incorporated, like like a yeah, that was his

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Speaker 1: was really married to this name for a while, um,

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Speaker 1: which is terrible, and a lot of his friends tell

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Speaker 1: him it's a bad name. There's kind of this like

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Speaker 1: early Journey, where everyone around Jeff is being like, hey,

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Speaker 1: how about this as a name? How about this is

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Speaker 1: a name? Like one of his friends suggest, why don't

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Speaker 1: we just call it make it so dot com based

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Speaker 1: on like Captain Picard, who T and G is on

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Speaker 1: the air at the point, and that's like Jeff's favorite

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Speaker 1: Star Trek character. I think they decide not to do

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Speaker 1: that because I'm sure it would be copyright infringement. Um.

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Speaker 1: So they stick with Cadabra, even though everyone's trying to

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Speaker 1: talk him out of it. Um. He and Mackenzy get

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Speaker 1: to Seattle. They rent a three bedroom home, which is

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Speaker 1: their first headquarters. They operated out of the garage, and

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Speaker 1: they immediately set to work starting a business. Their first lawyer,

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Speaker 1: the first lawyer for what becomes Amazon, is the one

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Speaker 1: who finally talks Jeff out of Cadabra Incorporated as a name,

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Speaker 1: and he does so by pointing out that over the

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Speaker 1: phone it sounds like saying cadaver, and He's like, people,

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Speaker 1: as I got a yeah, that's not a bad point. Um.

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Speaker 1: Sitting together in their living room, m Z and Jeff

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Speaker 1: Bezos registered a number of domains with potential names Awake

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Speaker 1: dot com, brows dot com, book Mall dot com. The

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Speaker 1: name they kept coming back to was relentless dot com.

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Speaker 1: They really like this. Jeff loves that name. He like

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Speaker 1: I think he prefers it to Amazon. I believe it

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Speaker 1: is still his favorite possible name for his company, Relentless.

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Speaker 1: That's what he thinks is the best possible name. I

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Speaker 1: think it's shit. Um. I think Amazon's a fine name

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Speaker 1: for a company. Yeah, relentless is a bad name. But

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Speaker 1: Jeff never is able to really let this go. He

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Speaker 1: buys the relentless dot com domain in n He really

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Speaker 1: tries to make this happen again. His friends kind of

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Speaker 1: come together, friends and like early employees, and eventually you're like,

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Speaker 1: it's creepy, Jeff, Like, it's a creepy name for your store.

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Speaker 1: People are not going it's not going to be good

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Speaker 1: for us UM and Amazon. He finally gets convinced to

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Speaker 1: pick Amazon, and the reason they decide on it is

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Speaker 1: that it kind of speak to his how grand his

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Speaker 1: goal is because the Amazon is the biggest river in

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Speaker 1: the world, right um, But it also is is it's

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Speaker 1: a placid name. It's not a name that scares anybody.

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Speaker 1: But Jeff never gives up on relentless dot com entirely.

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Speaker 1: And in fact, Jake, if you open a browser right

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Speaker 1: now and you type in relentless dot com, it'll take

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Speaker 1: you to Amazon. Well and dot com. Jesus Christ couldn't

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Speaker 1: even give it to the drinks company, just like nope. Yeah,

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Speaker 1: And it's one of those things. It's this, it's what

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Speaker 1: we've been kind of coming back to and back to.

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Speaker 1: It's like he's never really changed as a person, Like

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Speaker 1: he liked everything about him today is the same as

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Speaker 1: when he was in sixth grade pretty much. And obviously

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Speaker 1: the fact like he wanted relentless dot com to be

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Speaker 1: the name. I don't think he every gets over that,

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Speaker 1: um relentless, that it's relentless energy dot com. That's so long, man,

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Speaker 1: he can sell it to them. Yeah, another one of

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Speaker 1: Amazon's victims, that energy company. You know who else is

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Speaker 1: a victim of Amazon? Jeff Well, Jeff Jake, the products

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Speaker 1: and services that support this podcast. Yeah, actually, I'm sure

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Speaker 1: a number of them sell products on Amazon. But whatever,

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Speaker 1: we don't need to think about that as uh we

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Speaker 1: are back so uh. The same month that Jeff bought

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Speaker 1: the relentless dot com domain, he drove down to Portland,

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Speaker 1: Oregon to take a four day course on book selling

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Speaker 1: sponsored by the American Booksellers Association. He learned the ropes

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Speaker 1: of the business, and soon they were off selling books

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Speaker 1: at a slow pace at first. UM and Jeff and

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Speaker 1: his first couple of employees are like physically because the

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Speaker 1: way it works is they're they're buying books from one

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Speaker 1: of the two distributors who sells books to all the bookstores.

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Speaker 1: Like they get in order. They order the book from

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Speaker 1: the distributor, and then the distributorship the book to them,

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Speaker 1: and then they mail the book to the customer. Right.

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Speaker 1: They don't have a warehouse, they have a garage, so

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Speaker 1: like they're getting books as they're ordered and their painstakingly

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Speaker 1: mailing out each book themselves. And Jeff is to his credit,

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Speaker 1: involved in all of the ship work from the start,

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Speaker 1: Like he's putting stuff in packages, he's picking up deliveries,

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Speaker 1: he's mailing them off, and so is Mackenzie. And so

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Speaker 1: we're like they're all kind of doing everything right, all

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Speaker 1: of the early employees are doing every part of running

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Speaker 1: the business. There's no there's no like where there's no

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Speaker 1: warehouse staff, right, Like Jeff is the warehouse and so

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Speaker 1: is Mackenzie. Like everyone is everything in early Amazon. Um

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Speaker 1: that said, when it came to funding Amazon, other people

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Speaker 1: did most of the heavy lifting. Amazon did start with

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Speaker 1: ten thousand dollars that Jeff Bezos had saved up himself,

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Speaker 1: and another eight four thousand dollars in what are generally

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Speaker 1: referred to as interest free loans. Now, a lot of

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Speaker 1: this company this money actually came from his first employees.

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Speaker 1: He would basically when he was bringing he would he

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Speaker 1: was hiring people he'd met elsewhere in business and saying like, look,

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Speaker 1: you come to this company, take a pay cut, and

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Speaker 1: if you pay I'll let you buy stock now, right,

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Speaker 1: and like that's a big part of how he founds

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Speaker 1: the early company. So for example, um, his first employee

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Speaker 1: is a guy named Shell Kaplan, who many consider the

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Speaker 1: co founder of the company and who Jeff later kind

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Speaker 1: of betrays. Shell was Jeff's good friend, and he joins

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Speaker 1: Amazon because he believes in the vision of the company

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Speaker 1: so much that he takes a fifty pay cut and

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Speaker 1: he pays five grand to buy stock when he joins

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Speaker 1: in order to help fund the company. Oh he must

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Speaker 1: be fucking loaded enough. Oh yeah, I mean he's yes,

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Speaker 1: he did very very well. And in fact, like he

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Speaker 1: considered buying more but didn't want to gamble that much.

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Speaker 1: And it's like, I mean, it worked out well for him. Um,

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Speaker 1: Jeff's parents were the first angel investors he had, dumping

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Speaker 1: a hundred thousand dollars of their oil money into the venture.

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Speaker 1: In early nine, Jeff told his parents there was a

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Speaker 1: seventy chance they'd lose it all, explaining, I want you

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Speaker 1: to know what the risks are because I still want

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Speaker 1: to come home for Thanksgiving if this doesn't work. UM,

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Speaker 1: which is at least respectable. Uh. From the beginning, Amazon's

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Speaker 1: promise to customers was a bookstore that was always in stock.

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Speaker 1: Now they were really just letting regular people order books

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Speaker 1: the same way that bookstores did, straight from the people

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Speaker 1: who actually like kept them in a warehouse and charging

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Speaker 1: a premium for the privilege. The book distributors had a

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Speaker 1: rule where they'd only ship a book if you ordered

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Speaker 1: ten at a time. Since Amazon wasn't selling enough books

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Speaker 1: to to order ten books at a time generally, the

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Speaker 1: company developed a sneaky strategy of ordering the book they needed,

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Speaker 1: and like nine copies of an out of print book,

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Speaker 1: if the books were out of stock or out of print,

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Speaker 1: the order would get auto canceled by the ordering software UM,

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Speaker 1: and the one book that Amazon needed would get shipped

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Speaker 1: their way. And so that's how they got over this

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Speaker 1: limit was kind of like fucking with the system and

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Speaker 1: and hurting this company's profitability that they were utterly reliant on.

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Speaker 1: But at this stage, it's not really a big enough

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Speaker 1: business that anyone notices what they're doing. Um, In its

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Speaker 1: first year of operation, Amazon required another cash infusion from

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Speaker 1: Jeff's parents, this one of a hundred and forty five

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Speaker 1: thousand dollars. So fucking hell he gets I mean, he's

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Speaker 1: kind of funded by x on Mobile, you know, like

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Speaker 1: his dad gets rich from x on Mobile, and that's

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Speaker 1: what allows Amazon to survive its first year or two.

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Speaker 1: What what I don't get is why did he need

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Speaker 1: those big lumps of money from his parents. You know,

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Speaker 1: you already had like a really good job. I know

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Speaker 1: he quit, but did he not like save anything, you

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Speaker 1: know what I mean? I don't know. You know, it's

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Speaker 1: an idea too. I think tim grand which is what

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Speaker 1: he invested in, its more money. I don't, I don't

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Speaker 1: he wasn't. I don't know if it's that he made

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Speaker 1: his parents put most of the investment in, or if

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Speaker 1: he just like legitimately that was kind of what you

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Speaker 1: made in fintech at that point. I really don't know

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Speaker 1: what his actual net worth was. Um, but yeah, I

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Speaker 1: mean it's it is kind of worth noting as a

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Speaker 1: number of people will always point out that like, again,

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Speaker 1: he's he's he's very reliant on his his family money

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Speaker 1: in order to uh, you know, a couple of hundred grand.

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Speaker 1: It's not an insignificant investment against the thing that all

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Speaker 1: these guys have in common. Um. That said, Bezos and

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Speaker 1: his early employees knew that like mom and dad's money

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Speaker 1: ain't gonna keep going forever, and they have to pound

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Speaker 1: the pavement to find investors. It was not an easy

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Speaker 1: sell at first Amazon. The year his mom and dad

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Speaker 1: put a hundred and four you have granted into the business,

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Speaker 1: the company lost three hundred thousand dollars. Um. Amazon is

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Speaker 1: a lost leader for a while. Jeff told every potential

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Speaker 1: investor that the company had a seventy chance of failing,

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Speaker 1: but he projected net sales of seventy four million dollars

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Speaker 1: by the year two thousands. So he's like, yeah, we're

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Speaker 1: losing money now, but we'll be making tens of millions

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Speaker 1: of dollars by the year two thousand. And it's one

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Speaker 1: of those things I think at the time most people

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Speaker 1: would have probably rightly been like, well that is a

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Speaker 1: is nonsense, Like you're just you're just pulling a number

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Speaker 1: out of your ass. He was actually pessimistic with that estimate,

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Speaker 1: because actual net sales for Amazon in the year two

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Speaker 1: thousand were one point six four billion dollars um. Now,

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Speaker 1: Amazon still loses money in the year two thousand up

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Speaker 1: until like two thousand fifteen. I think they're losing money constantly. Uh,

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Speaker 1: but their income grows and that's why people keep investing

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Speaker 1: in them, and then they keep like reinvesting their profits

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Speaker 1: back into expanding the business. Um. So the investment money

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Speaker 1: starts to come in. N venture capitalists port tens of

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Speaker 1: millions of dollars into Amazon, and things begin moving very

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Speaker 1: fast for Jeff. Amazon's first company motto was get Big Fast,

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Speaker 1: which is also the motto for a significant portion of

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Speaker 1: steroid twitter. Um. And I guess we can make a

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Speaker 1: comment about all the testosterone Jeff appears to be taking

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Speaker 1: these days. But yeah, he got big fast, that's for sure. Yeah.

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Speaker 1: The company had its I p O in nine. It

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Speaker 1: succeeded in winning a stupid legal fight with Barnes and Noble,

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Speaker 1: who are like desperate to try to kill Amazon, but

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Speaker 1: not quite smart enough to figure out how UM and

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Speaker 1: it expands from books to CDs, DVDs, toys, and electronics.

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Speaker 1: In his first letter to shareholders, Jeff Bezos and some

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Speaker 1: of his early employees wrote, this is day one for

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Speaker 1: the Internet, and if we execute well day one for

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Speaker 1: Amazon dot Com. Now that is important because it's become

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Speaker 1: kind of like a religion within Amazon corporate culture. You

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Speaker 1: will hear day one constantly. There's Day one posters everywhere.

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Speaker 1: Jeff regularly emphasizes the need for day one thinking. Treating

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Speaker 1: what they're doing like it's the first time anybody's done

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Speaker 1: it again. Not a bad way too, obviously, this is

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Speaker 1: a part of the company's success. That's not a bad

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Speaker 1: way to look at things if you're trying to innovate them. UM.

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Speaker 1: For its first few years, Amazon grew meteorically. Bezos was

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Speaker 1: Person of the Year in nineteen, which actually causes some

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Speaker 1: problems for him because the article itself and all the

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Speaker 1: attention generated by this fawning piece helps to send a

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Speaker 1: flood of customers to Amazon for holiday season, and this

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Speaker 1: is the first time Amazon has a huge holiday season.

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Speaker 1: They've recently expanded to products other than books. Um, and

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Speaker 1: they get all this media attention and people start flowing

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Speaker 1: flooding there to like buy Christmas presents and Hanakah presents

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Speaker 1: and stuff from them. Um and Bezos had put millions

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Speaker 1: in investment capital into expanding Amazon for this moment. This

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Speaker 1: had been his goal and was part of the strategy.

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Speaker 1: But they kind of suck it up because actually keeping

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Speaker 1: number one, keeping the most popular toys and stock proved

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Speaker 1: to be kind of impossible. They were selling too fast,

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Speaker 1: but a bunch of other stuff they bought didn't sell,

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Speaker 1: and so like their warehouses are filled with unsold inventory

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Speaker 1: while people can't get the products that they actually want. Um.

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Speaker 1: It's enough of like a cluster fuck, and largely caused

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Speaker 1: by issues with the warehouse right, Like he hadn't scaled

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Speaker 1: up distribution enough in order to actually handle the demand.

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Speaker 1: They've only got like five warehouses at this point. So

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Speaker 1: this causes enough of a calamity that it starts. It

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Speaker 1: results in this like flood of bad press. Journalists start

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Speaker 1: writing articles about how Amazon stock is overvalued and like

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Speaker 1: the company is about to come to a crashing end,

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Speaker 1: and it's actually really kind of an existential threat for Amazon,

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Speaker 1: and in order to kind of meet the threat, Bezos

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Speaker 1: institutes what he calls a Save Santa operation. He forces

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Speaker 1: his like all of his employees, like the guys who

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Speaker 1: are like coding, the people who are like doing marketing,

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Speaker 1: the people who are like all of his like actual

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Speaker 1: staff employees, he forces to take two weeks each leave

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Speaker 1: their families and spend two weeks of the holidays staffing

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Speaker 1: customer service lines or working distribution in like isolated warehouses.

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Speaker 1: To save money, workers were kept two to a hotel room.

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Speaker 1: And I'm gonna quote from the Everything Store here to

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Speaker 1: talk about this first big Amazon holiday season infirmly. Some

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Speaker 1: employees stayed at the Golden Nugget in Reno, and after

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Speaker 1: they worked at the graveyard shift, they met for beers

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Speaker 1: at the casino bar at six am. Later, a few

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Speaker 1: swore they had been working alongside furloughed prisoners from a

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Speaker 1: nearby jail, though that is difficult to prove. Early employee

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Speaker 1: Tom shon Hoff was among the group that went to Delaware,

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Speaker 1: where the facility was having problems with the quality of

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Speaker 1: the temporary labor pool. There were a lot of temp

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Speaker 1: workers that looked like the rehab center had pushed them

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Speaker 1: out the back door. He says he watched one worker

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Speaker 1: get fired for intoxication and then wet himself while he

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Speaker 1: tried to protest. So, yeah, we're starting to see the beginnings,

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Speaker 1: right of like the Jeff Wayno today, it was enough

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Speaker 1: of a debacle that some executives and it's kind of

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Speaker 1: a mix of how badly the Christmas season goes and

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Speaker 1: how like how infuriated they are by this time article

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Speaker 1: where he like is fluffing himself and that leads to

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Speaker 1: a lot of this rush on Amazon. Some executives try

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Speaker 1: to push him out of his company. They call him

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Speaker 1: impetuous and controlling Um, and this sparks a fight with

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Speaker 1: the board and alleged fight with the board. Some board

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Speaker 1: members deny it happened. There are there are other people

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Speaker 1: who are executives at the time who say it happened.

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Speaker 1: There's this fight to try to oust Jeff Um. And

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Speaker 1: this is not that long after uh, Steve Jobs had

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Speaker 1: been ousted from Apple, right, there's this period where Jobs

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Speaker 1: gets forced out of Apple. He creates a computer company,

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Speaker 1: he creates pixar Um. Jeff manages to avoid this happening

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Speaker 1: because he keeps the majority of Amazon stock, but the

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Speaker 1: early years are rough, and you know, there's a lot

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Speaker 1: of people saying Amazon and is dead in the like

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Speaker 1: two thousand, two thousand one, people saying the company is

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Speaker 1: never going to survive another couple of years. Bezos actually

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Speaker 1: gets investigated for insider trading in two thousand one because

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Speaker 1: of like how bad some of the financial decisions he

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Speaker 1: makes are. Um and the company is just bleeding money

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Speaker 1: through its early years, like their their profits are increasing rapidly,

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Speaker 1: but they're just like pissing cash into a fucking trash fire.

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Speaker 1: That said, by the early odds, things had started to

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Speaker 1: stabilize and the company began to grow like exponentially. It's

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Speaker 1: profitability again. They're still losing money, but they're growing profits

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Speaker 1: by so much that investors are willing to keep pumping

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Speaker 1: money into the company because they're like, well, if we

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Speaker 1: stop reinvesting in the company, eventually there will be dividends

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Speaker 1: and shipped for us. It's just yeah, you know that

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Speaker 1: that's kind of like how he sells this to them, um,

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Speaker 1: And you know the broad strokes of the rest of

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Speaker 1: the story, Jake, everybody does because we've all lived through it, right.

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Speaker 1: Amazon expands steadily and despite losing to Apple in the

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Speaker 1: digital music race because Jeff Bezos doesn't understand music. The

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Speaker 1: company rises is to become the globe striding colossus we

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Speaker 1: all know today. To close us out, then, I think

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Speaker 1: the best thing I could do, rather than sort of like, oh,

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Speaker 1: let's talk about how they started Amazon Web Services, or

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Speaker 1: let's talk about how they expanded in the television, right,

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Speaker 1: I think the best way I could actually close us

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Speaker 1: out today is to hone in on the first stirrings

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Speaker 1: of two problems that are kind of the main things

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Speaker 1: Amazon gets criticized for today. Number one would be it's relentless,

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Speaker 1: obsessive and unhealthy work culture, and number two would be

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Speaker 1: the callous disregard for human warehouse workers in its distribution

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Speaker 1: centers or fulfillment centers, as they call it. The first

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Speaker 1: major publication to report on Amazon's corporate culture in a

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Speaker 1: really critical way with The New York Times in two

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Speaker 1: thousand fifteen. Here's how it opened. They are told to

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Speaker 1: forget the poor habits they learned at previous jobs. One

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Speaker 1: employee recalled when they hit the wall from unrelenting pace.

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Speaker 1: There is only one solution. Climbed the wall. Others reported

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Speaker 1: to be the best Amazon ians they can be. They

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Speaker 1: should be guided by the leadership Principles fourteen rules inscribed

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Speaker 1: on handy laminated cards when quizzed days later those with

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Speaker 1: perfect scores or in a virtual award proclaiming I'm peculiar,

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Speaker 1: the company's proud phrase for overturning workplace conventions and one

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Speaker 1: of the things that really separates Amazon from the other

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Speaker 1: big dot com startups like Google. They don't go to

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Speaker 1: any real efforts to make it comfortable or to like

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Speaker 1: give perks to employees, like you get money and you'll

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Speaker 1: get stock and you can get rich working at Amazon.

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Speaker 1: But like there is like there's no like massage rooms.

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Speaker 1: There's no like game rooms for chilling out in like

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Speaker 1: Google gets famous for. Like Amazon, Like, yeah, you are

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Speaker 1: going to work like yourself to the bone at Amazon.

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Speaker 1: That's the promise we make you um and the times

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Speaker 1: sites employees like bo Olsen, a bookmarketer who said his

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Speaker 1: main memory of Amazon was seeing people weep at their desks.

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Speaker 1: You walk out of a conference room and you'll see

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Speaker 1: a grown man covering his face. Nearly every person I

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Speaker 1: worked with I saw cry at their desk. Much of

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Speaker 1: the heart nous and irrational workaholism was justified by the

430
00:22:02,680 --> 00:22:07,080
Speaker 1: near sanctified fourteen rules Bezos had for his employees. Quote.

431
00:22:08,200 --> 00:22:11,520
Speaker 1: According to early executives and employees, Mr Bezos was determined

432
00:22:11,520 --> 00:22:14,360
Speaker 1: almost from the moment he founded Amazon in nineteen four

433
00:22:14,600 --> 00:22:18,680
Speaker 1: to resist the forces he thought sapped businesses over time, bureaucracy,

434
00:22:18,800 --> 00:22:21,960
Speaker 1: profligate spending, lack of rigor. As the company grew, he

435
00:22:22,000 --> 00:22:24,800
Speaker 1: wanted to codify his ideas about the workplace, some of

436
00:22:24,840 --> 00:22:28,480
Speaker 1: them proudly counterintuitive, onto instructions simple enough for a new

437
00:22:28,480 --> 00:22:31,640
Speaker 1: worker to understand, general enough to apply to the nearly

438
00:22:31,680 --> 00:22:34,520
Speaker 1: limitless number of businesses he wanted to enter, and stringent

439
00:22:34,640 --> 00:22:37,959
Speaker 1: enough to stave off the mediocrity he feared. The result

440
00:22:38,000 --> 00:22:40,960
Speaker 1: was the Leadership Principles, the articles of faith that described

441
00:22:40,960 --> 00:22:44,000
Speaker 1: the way Amazonian should act. In contrast to companies where

442
00:22:44,040 --> 00:22:47,680
Speaker 1: declarations about their philosophy amount to vague platitudes, Amazon has

443
00:22:47,760 --> 00:22:50,120
Speaker 1: rules that are part of its daily language and rituals

444
00:22:50,320 --> 00:22:52,960
Speaker 1: used in hiring, cited at meetings, and quoted in food

445
00:22:52,960 --> 00:22:56,399
Speaker 1: truck lines at lunchtime. Some Amazonians say they teach them

446
00:22:56,480 --> 00:22:59,960
Speaker 1: to their children. The guidelines conjure and Empire of elite

447
00:23:00,000 --> 00:23:03,160
Speaker 1: workers principle number five higher and develop the best who

448
00:23:03,200 --> 00:23:06,280
Speaker 1: hold one another to towering expectations and are liberated from

449
00:23:06,280 --> 00:23:09,480
Speaker 1: the forces red tape office politics that keep them from

450
00:23:09,480 --> 00:23:13,200
Speaker 1: developing their Utmost employees are to exhibit ownership number two

451
00:23:13,280 --> 00:23:15,680
Speaker 1: or mastery of every emblement of their businesses and to

452
00:23:15,800 --> 00:23:19,159
Speaker 1: dive deep number twelve or find the underlying ideas that

453
00:23:19,200 --> 00:23:22,800
Speaker 1: can fix problems or identify new services before shoppers even

454
00:23:22,840 --> 00:23:28,320
Speaker 1: ask for them. And the indoctrination that that goes on

455
00:23:28,359 --> 00:23:30,800
Speaker 1: in Amazon that like to get people kind of roped

456
00:23:30,840 --> 00:23:33,560
Speaker 1: into this idea. The overwork that comes with it is

457
00:23:33,600 --> 00:23:38,480
Speaker 1: also consuming. That employees take to calling themselves ammabots with

458
00:23:38,560 --> 00:23:41,040
Speaker 1: a myth mix of like pride and resignation, like there's

459
00:23:41,040 --> 00:23:43,520
Speaker 1: an element of pride that were like robots for Jeff's

460
00:23:43,520 --> 00:23:47,400
Speaker 1: big vision. Being the ultimate data guy, Jeff takes full

461
00:23:47,440 --> 00:23:50,679
Speaker 1: advantage of the fact that Amazon's online nature gives him

462
00:23:50,760 --> 00:23:53,200
Speaker 1: like a wider variety of metrics than any other boss

463
00:23:53,200 --> 00:23:56,040
Speaker 1: has ever had to analyze and judge how the human

464
00:23:56,040 --> 00:23:59,640
Speaker 1: beings in his company are working. Employee evaluations are often

465
00:23:59,720 --> 00:24:02,840
Speaker 1: fifth to sixty pages long, filled with numbers like on

466
00:24:03,000 --> 00:24:06,159
Speaker 1: people's performance that employees are expected to memorize, and then

467
00:24:06,200 --> 00:24:08,560
Speaker 1: they'll get cold called by their managers. And if they

468
00:24:08,600 --> 00:24:11,159
Speaker 1: haven't memorized all the different numbers about their metrics and

469
00:24:11,200 --> 00:24:14,440
Speaker 1: aren't able to like explain or justify them, they get

470
00:24:14,440 --> 00:24:18,199
Speaker 1: in trouble quote from The Times. Explanations like we're not

471
00:24:18,280 --> 00:24:21,080
Speaker 1: totally sure or all get back to you are not acceptable,

472
00:24:21,119 --> 00:24:24,880
Speaker 1: many employees said. Some managers sometimes dismissed such responses as

473
00:24:24,920 --> 00:24:29,239
Speaker 1: stupid or told the workers to just stop it. And

474
00:24:29,280 --> 00:24:32,600
Speaker 1: in this Amazon is very much taking after its founder.

475
00:24:32,640 --> 00:24:34,440
Speaker 1: And I'm gonna quote here from the Everything sort of

476
00:24:34,440 --> 00:24:37,440
Speaker 1: talk about how Jeff actually refers to like speaks directly

477
00:24:37,480 --> 00:24:40,840
Speaker 1: to the people who work for him. Bezos was prone

478
00:24:40,840 --> 00:24:45,720
Speaker 1: to melodramatic temper tantrums that some Amazon employees called privately nutters.

479
00:24:45,720 --> 00:24:49,320
Speaker 1: A colleague failing to meet Bezos's exacting standards would predictably

480
00:24:49,359 --> 00:24:51,760
Speaker 1: set off a nutter if an employee did not have

481
00:24:51,800 --> 00:24:53,800
Speaker 1: the right answers, or tried to bluff the right answer,

482
00:24:53,880 --> 00:24:56,119
Speaker 1: or took credit for someone else's work, or exhibited a

483
00:24:56,119 --> 00:24:59,000
Speaker 1: whiff of internal politics, or showed any kind of uncertainty

484
00:24:59,080 --> 00:25:01,840
Speaker 1: or frailty in the heat of battle. The vessel in Basos,

485
00:25:01,920 --> 00:25:04,480
Speaker 1: his forehead popped out and his filter fell away. He

486
00:25:04,600 --> 00:25:07,359
Speaker 1: was capable of both hyperbole and cruelty in these moments,

487
00:25:07,359 --> 00:25:10,600
Speaker 1: and over the years he delivered some devastating rebukes to employees.

488
00:25:10,880 --> 00:25:14,040
Speaker 1: Among his greatest hits collected and relaid by Amazon veterans,

489
00:25:14,040 --> 00:25:16,879
Speaker 1: are I'm sorry? Did I take my stupid pills today?

490
00:25:17,119 --> 00:25:18,919
Speaker 1: Are you trying to take credit for something you had

491
00:25:18,960 --> 00:25:21,760
Speaker 1: nothing to do with? Are you lazy or just incompetent?

492
00:25:22,040 --> 00:25:24,679
Speaker 1: I trust you to one run world class operations, and

493
00:25:24,720 --> 00:25:27,080
Speaker 1: this is another example of how you are letting me down.

494
00:25:27,440 --> 00:25:29,359
Speaker 1: If I hear that idea again, I'm going to have

495
00:25:29,440 --> 00:25:31,920
Speaker 1: to kill myself. Does it surprise you that I don't

496
00:25:31,920 --> 00:25:34,000
Speaker 1: know that that you don't know the answer to that question?

497
00:25:34,200 --> 00:25:38,119
Speaker 1: And why are you ruining my life? Uh? Some of

498
00:25:38,119 --> 00:25:41,359
Speaker 1: them are pretty funny. I mean it's pretty bad, but

499
00:25:41,440 --> 00:25:45,240
Speaker 1: I wouldn't say that, like you know, And you can

500
00:25:45,240 --> 00:25:47,600
Speaker 1: see with that both how someone could be like really

501
00:25:47,840 --> 00:25:50,240
Speaker 1: fucked up by that, especially if like you devote your

502
00:25:50,240 --> 00:25:52,040
Speaker 1: whole life to this company and in your boss just

503
00:25:52,280 --> 00:25:54,840
Speaker 1: basically said you're ruining. But you can also see how

504
00:25:54,880 --> 00:25:58,000
Speaker 1: people would find that like intoxicating and would draw into

505
00:25:58,040 --> 00:26:00,159
Speaker 1: a guy like that. And would be desperate to like

506
00:26:00,280 --> 00:26:03,399
Speaker 1: please him and get positive Like when it's paired with

507
00:26:03,480 --> 00:26:08,280
Speaker 1: all that rules, yeah, it's very cold, like yeah, exactly.

508
00:26:08,600 --> 00:26:10,399
Speaker 1: So you can see both white people would find it

509
00:26:10,400 --> 00:26:12,879
Speaker 1: off putting, but also why that would inspire people to

510
00:26:12,920 --> 00:26:16,520
Speaker 1: work even harder, you know. Um. In two thousand thirteen,

511
00:26:16,560 --> 00:26:20,080
Speaker 1: Amazon hired Elizabeth Willett, a former Army captain who had

512
00:26:20,119 --> 00:26:23,000
Speaker 1: served in Iraq. After some time, she had a child,

513
00:26:23,080 --> 00:26:24,920
Speaker 1: and she arranged with her boss to come to work

514
00:26:24,960 --> 00:26:27,040
Speaker 1: earlier and leave earlier so that she could pick up

515
00:26:27,040 --> 00:26:28,760
Speaker 1: her baby and then she would get on her laptop

516
00:26:28,800 --> 00:26:31,399
Speaker 1: and work from home. Her boss told her this was fine,

517
00:26:31,520 --> 00:26:34,159
Speaker 1: but her colleagues thought she was skipping work because she

518
00:26:34,200 --> 00:26:36,000
Speaker 1: was leaving early and they didn't know she was coming

519
00:26:36,040 --> 00:26:38,480
Speaker 1: in early, and they informed on her to the boss

520
00:26:38,520 --> 00:26:41,320
Speaker 1: that she'd gotten approval from to do this. That boss

521
00:26:41,400 --> 00:26:44,080
Speaker 1: then attacked her, saying, I can't stand here and defend

522
00:26:44,080 --> 00:26:46,480
Speaker 1: you if your peers are saying you're not doing your work.

523
00:26:47,080 --> 00:26:50,440
Speaker 1: And this is kind of emblematic of a dark trended Amazon,

524
00:26:50,520 --> 00:26:53,520
Speaker 1: which is that a lot of these these fourteen principles

525
00:26:53,560 --> 00:26:56,359
Speaker 1: and the system of coworker evaluations often based on the

526
00:26:56,400 --> 00:27:00,600
Speaker 1: principles wind up being used uncommonly, often to attack specific

527
00:27:00,600 --> 00:27:02,639
Speaker 1: groups of employees. And one of the things that like

528
00:27:02,680 --> 00:27:04,359
Speaker 1: women who work there will note is that one of

529
00:27:04,359 --> 00:27:06,879
Speaker 1: these fourteen principles is you have to be honest. And

530
00:27:06,920 --> 00:27:09,560
Speaker 1: one of the pieces of criticism that like female employees

531
00:27:09,600 --> 00:27:12,800
Speaker 1: get from their coworkers at Amazon is that they're all dishonest.

532
00:27:12,960 --> 00:27:15,680
Speaker 1: Like that's that's a common it's a part of why

533
00:27:15,960 --> 00:27:18,560
Speaker 1: there's very few women in high management. And one of

534
00:27:18,560 --> 00:27:20,679
Speaker 1: the claims they'll make is that like, well, these principles

535
00:27:20,880 --> 00:27:24,520
Speaker 1: are used as a justification to attack us and just

536
00:27:24,560 --> 00:27:26,600
Speaker 1: say that like, well, you're not following the principles, when

537
00:27:26,600 --> 00:27:30,240
Speaker 1: in reality, it's just like people kind of being shitty

538
00:27:30,280 --> 00:27:32,719
Speaker 1: towards women in the way that they often are. Um,

539
00:27:32,760 --> 00:27:36,119
Speaker 1: it's an allegation, very very whole, like that ship, you

540
00:27:36,119 --> 00:27:38,919
Speaker 1: know what I mean, that's actually and it's like mind games,

541
00:27:38,960 --> 00:27:41,280
Speaker 1: that's like abusive type ship, Like right, he gave her

542
00:27:41,280 --> 00:27:43,959
Speaker 1: the permission and then he's having to go for something

543
00:27:44,000 --> 00:27:47,240
Speaker 1: that you know, essentially he should have told the colleagues

544
00:27:47,320 --> 00:27:50,080
Speaker 1: it's none of your business, like stop informing, Like but no,

545
00:27:50,240 --> 00:27:53,080
Speaker 1: it's like now that person is in trouble that's fucking weird.

546
00:27:53,119 --> 00:27:55,760
Speaker 1: That's not right well, and it's again because Jeff isn't

547
00:27:55,760 --> 00:27:58,120
Speaker 1: directly involved in that story, but the system he's builts.

548
00:27:58,160 --> 00:28:00,359
Speaker 1: And part of why that, part of why that boss

549
00:28:00,400 --> 00:28:02,200
Speaker 1: is doing that is that that boss is going to

550
00:28:02,240 --> 00:28:06,000
Speaker 1: get evaluated by all of their employees. And so the boss,

551
00:28:06,040 --> 00:28:07,840
Speaker 1: even though he gave her permission to do this, even

552
00:28:07,840 --> 00:28:10,560
Speaker 1: though she's been doing a great job, he can't defend

553
00:28:10,560 --> 00:28:15,080
Speaker 1: her because then his subordinates will criticize him, and so

554
00:28:15,160 --> 00:28:17,479
Speaker 1: he throws her under the bus to protect his own career.

555
00:28:17,680 --> 00:28:20,760
Speaker 1: And that's the that is how the system Jeff built works, right,

556
00:28:20,800 --> 00:28:24,199
Speaker 1: That's it working as intended um, which is kind of

557
00:28:24,200 --> 00:28:27,720
Speaker 1: sucked up, I would argue, uh, and it gets sucked

558
00:28:27,760 --> 00:28:29,679
Speaker 1: up at her because again, that story is one of

559
00:28:29,720 --> 00:28:33,880
Speaker 1: the less unsettling ones from the Times. A woman who

560
00:28:33,880 --> 00:28:36,760
Speaker 1: had thyroid cancer was given a low performance rating after

561
00:28:36,800 --> 00:28:39,720
Speaker 1: she returned from treatment. She says her manager explained that

562
00:28:39,720 --> 00:28:42,400
Speaker 1: while she was out, her peers were accomplishing a great deal.

563
00:28:42,760 --> 00:28:45,920
Speaker 1: Another employee who miscarried twins left for a business trip

564
00:28:45,960 --> 00:28:48,760
Speaker 1: the day after she had surgery. I'm sorry, the work

565
00:28:48,840 --> 00:28:50,960
Speaker 1: is still going to need to get done, she said,

566
00:28:50,960 --> 00:28:53,040
Speaker 1: her boss told her, from where you are in life,

567
00:28:53,080 --> 00:28:54,959
Speaker 1: trying to start a family, I don't know if this

568
00:28:55,040 --> 00:28:57,240
Speaker 1: is the right place for you. A woman who had

569
00:28:57,280 --> 00:28:59,200
Speaker 1: breast cancer was told that she was being put on

570
00:28:59,280 --> 00:29:02,680
Speaker 1: a performance improvement plan, Amazon code for you're in danger

571
00:29:02,720 --> 00:29:06,040
Speaker 1: of being fired because difficulties in her personal life had

572
00:29:06,080 --> 00:29:09,360
Speaker 1: interfered with her fulfilling her work goals. Their accounts echoed

573
00:29:09,360 --> 00:29:11,680
Speaker 1: others from workers who had suffered health crises and felt

574
00:29:11,680 --> 00:29:14,040
Speaker 1: they had also been judged harshly instead of being given

575
00:29:14,080 --> 00:29:18,560
Speaker 1: time to recover. And that's not unique at Amazon. It's

576
00:29:18,600 --> 00:29:22,240
Speaker 1: just again, it's part of this system that that that

577
00:29:22,240 --> 00:29:25,000
Speaker 1: that Jeff has built. Now, when this report, this New

578
00:29:25,080 --> 00:29:28,400
Speaker 1: York Times article drops, Amazon makes statements about like we're

579
00:29:28,400 --> 00:29:30,600
Speaker 1: going to investigate a lot of these claims, like this

580
00:29:30,680 --> 00:29:32,840
Speaker 1: is not us, Jeff Bezo, since that a letter saying

581
00:29:32,840 --> 00:29:35,520
Speaker 1: like what's described in this New York Times article isn't

582
00:29:35,520 --> 00:29:39,120
Speaker 1: the Amazon that I know or that our employees know. Um, yeah,

583
00:29:39,200 --> 00:29:41,880
Speaker 1: it's the standard kind of pr flak response to this ship.

584
00:29:42,200 --> 00:29:45,840
Speaker 1: There are some changes made to the employee self review process,

585
00:29:45,840 --> 00:29:48,959
Speaker 1: but fundamentally nothing changes about the way the company treats

586
00:29:49,000 --> 00:29:53,800
Speaker 1: its own people, because again, to Jeff, the human beings

587
00:29:53,840 --> 00:29:56,440
Speaker 1: that work for Amazon are both like robots that are

588
00:29:56,480 --> 00:29:59,200
Speaker 1: there to execute his ideas, but they're also there to

589
00:29:59,240 --> 00:30:01,760
Speaker 1: be targets for his anger when things go wrong. And

590
00:30:01,800 --> 00:30:04,080
Speaker 1: a good example of this comes from the first Prime Day,

591
00:30:04,160 --> 00:30:06,320
Speaker 1: which is Amazon's big sales day, and the very first

592
00:30:06,360 --> 00:30:10,200
Speaker 1: Prime Day occurs in two thousand fifteen. Um, Amazon programmers

593
00:30:10,200 --> 00:30:12,720
Speaker 1: and other staff work twenty four hour days in order

594
00:30:12,760 --> 00:30:15,680
Speaker 1: to prepare the site to host its first massive sales event,

595
00:30:15,840 --> 00:30:17,960
Speaker 1: which went on to be the biggest sales day in

596
00:30:18,040 --> 00:30:20,720
Speaker 1: company history. So this is a huge success by any

597
00:30:20,760 --> 00:30:22,880
Speaker 1: kind of financial metric. Right, they announced this thing, they

598
00:30:23,000 --> 00:30:25,360
Speaker 1: roll out this big day of sales, and it's it's

599
00:30:25,400 --> 00:30:28,840
Speaker 1: the best they've ever done, right, Um, But the rollout

600
00:30:28,880 --> 00:30:31,719
Speaker 1: of the software that organized the deals was imperfect. So,

601
00:30:31,760 --> 00:30:34,200
Speaker 1: like they're kind of algorithmically picking which stuff to put

602
00:30:34,240 --> 00:30:37,360
Speaker 1: on sale. Um, some of the products that they're relying

603
00:30:37,400 --> 00:30:39,400
Speaker 1: on to be big sellers sell out and so people

604
00:30:39,400 --> 00:30:42,320
Speaker 1: can't get them other like weird products, Like there's like

605
00:30:42,400 --> 00:30:44,520
Speaker 1: discounts on pop tarts and ship and people are like,

606
00:30:44,560 --> 00:30:47,080
Speaker 1: I can't get these electronics that obviously I want, but

607
00:30:47,080 --> 00:30:48,880
Speaker 1: you're trying to get me to buy these discounted pop

608
00:30:49,600 --> 00:30:51,720
Speaker 1: it's not it doesn't hurt again, it's the best sales

609
00:30:51,720 --> 00:30:54,160
Speaker 1: that the company has ever had. It doesn't hurt them financially.

610
00:30:54,560 --> 00:30:56,680
Speaker 1: But there's a bunch of people on like Twitter making

611
00:30:56,760 --> 00:31:00,600
Speaker 1: fun of Amazon because of some like technical glitches, and this,

612
00:31:00,720 --> 00:31:03,680
Speaker 1: to Jeff Bezos completely wipes out the fact that they've

613
00:31:03,680 --> 00:31:05,960
Speaker 1: made the most money they've ever made. People are making

614
00:31:05,960 --> 00:31:08,680
Speaker 1: fun of Amazon, and so he's furious at the people

615
00:31:08,680 --> 00:31:12,680
Speaker 1: who have just executed his dream to do Prime Day. Right. Um,

616
00:31:12,720 --> 00:31:15,040
Speaker 1: I think a reasonable person would be like, yes, of course,

617
00:31:15,040 --> 00:31:16,880
Speaker 1: some weird ship went wrong around the edges. We made

618
00:31:16,920 --> 00:31:21,000
Speaker 1: the most money ever. This is worth celebrating, Jeff. Yeah,

619
00:31:22,600 --> 00:31:29,720
Speaker 1: dollars going to buy. Yeah. But Jeff gets really fucking angry,

620
00:31:29,760 --> 00:31:32,320
Speaker 1: and I'm gonna quote here from the book Amazon Unbound.

621
00:31:32,920 --> 00:31:35,760
Speaker 1: Behind the scenes, Jeff lost his mind over the negative

622
00:31:35,800 --> 00:31:38,959
Speaker 1: online reactions, said Craig Berman, a senior PR vice president

623
00:31:38,960 --> 00:31:41,040
Speaker 1: who was at his son's swim meet in Oregon when

624
00:31:41,080 --> 00:31:43,360
Speaker 1: all hell broke loose. He was screaming at me and

625
00:31:43,400 --> 00:31:45,000
Speaker 1: my team that we needed to be clear that these

626
00:31:45,000 --> 00:31:48,440
Speaker 1: aren't shitty deals. He was being maniacal, saying get this fixed.

627
00:31:48,480 --> 00:31:52,360
Speaker 1: You've got to show this as a success, and yeah,

628
00:31:52,480 --> 00:31:56,000
Speaker 1: it's you know, it's it's the kind of like poisoned

629
00:31:56,000 --> 00:31:59,840
Speaker 1: internet brain a lot of big wealthy people have where

630
00:31:59,840 --> 00:32:02,920
Speaker 1: it's like, I mean, by any measure of the word success,

631
00:32:02,920 --> 00:32:04,720
Speaker 1: this is a success. But people are angry at you

632
00:32:04,800 --> 00:32:07,640
Speaker 1: on Twitter. So you're like, you're broken about it. I

633
00:32:07,640 --> 00:32:09,400
Speaker 1: I you know, I really kind of get my head

634
00:32:09,440 --> 00:32:11,280
Speaker 1: around that. I mean, you know, I'm one for Twitter

635
00:32:11,360 --> 00:32:14,160
Speaker 1: and that it's fine, Like it's decent. But if you

636
00:32:14,240 --> 00:32:17,600
Speaker 1: had all the money in the world, it's why, like,

637
00:32:17,640 --> 00:32:19,719
Speaker 1: oh are you with this person on Twitter? You can

638
00:32:19,760 --> 00:32:22,000
Speaker 1: go and do anything. I'd be on a quad bike,

639
00:32:22,160 --> 00:32:24,200
Speaker 1: like just doing something, you know what I mean, Like, oh,

640
00:32:24,280 --> 00:32:27,719
Speaker 1: someone's about on Twitter by Like it's just I it's

641
00:32:27,840 --> 00:32:29,400
Speaker 1: You're right, it's like a poison you know. I mean,

642
00:32:29,480 --> 00:32:31,840
Speaker 1: but I think it comes comes with ego, you know,

643
00:32:31,880 --> 00:32:35,840
Speaker 1: when everything you built. Yep. I think so it's because

644
00:32:35,880 --> 00:32:40,000
Speaker 1: this company is like fundamentally extremely personal to him. Yeah, exactly,

645
00:32:40,040 --> 00:32:46,200
Speaker 1: it is the core of his identity. Yep. Cool shit. Uh.

646
00:32:46,280 --> 00:32:49,960
Speaker 1: There's probably no better example of how uncaring and actively

647
00:32:49,960 --> 00:32:52,840
Speaker 1: harmful Jeff can be towards his employees than the story

648
00:32:52,920 --> 00:32:56,720
Speaker 1: of Amazon's fulfillment centers. We've all seen articles that dropped

649
00:32:56,760 --> 00:32:58,880
Speaker 1: early this year about people pissing in bottles to make

650
00:32:58,880 --> 00:33:02,600
Speaker 1: the inhuman metrics for cargo moved or packages delivered. Um,

651
00:33:02,680 --> 00:33:05,800
Speaker 1: you have a show, you know, Mega corp is largely

652
00:33:05,880 --> 00:33:07,760
Speaker 1: about this. This is a big part of Mega Corps

653
00:33:07,840 --> 00:33:12,240
Speaker 1: is like Amazon fulfillment center workers and how they're treated. Um.

654
00:33:12,360 --> 00:33:14,760
Speaker 1: And so at the end of this episode, I'd like

655
00:33:14,800 --> 00:33:17,120
Speaker 1: to talk about how that system started, right like the

656
00:33:17,240 --> 00:33:19,840
Speaker 1: very early days of what led to this people pissing

657
00:33:19,840 --> 00:33:21,880
Speaker 1: in bottles and like passing out and stuff on the

658
00:33:21,920 --> 00:33:26,040
Speaker 1: work floor and RV city setting up outside of fcs

659
00:33:26,040 --> 00:33:28,400
Speaker 1: in order to keep them like all that ship. So

660
00:33:28,840 --> 00:33:32,440
Speaker 1: the man who largely built Amazon's fulfillment center system was

661
00:33:32,520 --> 00:33:35,480
Speaker 1: Dave Clark. He was a former middle school band teacher

662
00:33:35,480 --> 00:33:38,200
Speaker 1: who got hired in nineteen nine when the company had

663
00:33:38,240 --> 00:33:41,400
Speaker 1: only seven warehouses in the US. By two thousand twelve,

664
00:33:41,400 --> 00:33:44,360
Speaker 1: when he becomes head of Global Operations, Amazon has forty

665
00:33:44,400 --> 00:33:47,680
Speaker 1: warehouses in the US and twenty four other warehouses worldwide,

666
00:33:48,240 --> 00:33:50,680
Speaker 1: but these were located mostly in the middle of nowhere.

667
00:33:50,720 --> 00:33:53,000
Speaker 1: So at this point twelve, Amazon has a bunch of

668
00:33:53,000 --> 00:33:55,760
Speaker 1: warehouses like sixty seventy in the world. But they're all

669
00:33:55,760 --> 00:33:58,280
Speaker 1: in like really inconvenient places. And the reason why they're

670
00:33:58,280 --> 00:34:00,680
Speaker 1: inconvenient places is those are general lead the places with

671
00:34:00,680 --> 00:34:04,040
Speaker 1: the chiefest labor and also the most advantageous for tax purposes.

672
00:34:04,040 --> 00:34:05,680
Speaker 1: So we'll set up like the warehouses will be in

673
00:34:05,760 --> 00:34:08,200
Speaker 1: some bum funk town in the middle of nowhere because

674
00:34:08,239 --> 00:34:10,680
Speaker 1: that town gives them a tax break and because it's

675
00:34:10,760 --> 00:34:13,480
Speaker 1: cheap to higher workers around there, right, And that makes

676
00:34:13,480 --> 00:34:16,040
Speaker 1: sense for a company that isn't trying to do what

677
00:34:16,120 --> 00:34:18,080
Speaker 1: Amazon starts trying to do. Right. That can work great

678
00:34:18,080 --> 00:34:20,840
Speaker 1: if you're just trying to sell products online minimize your costs.

679
00:34:21,160 --> 00:34:22,880
Speaker 1: But if you're obsessed with the idea that like there

680
00:34:22,920 --> 00:34:25,440
Speaker 1: should be two day or one day shipping, you can't

681
00:34:25,480 --> 00:34:26,880
Speaker 1: do that. You have to have a fat you have

682
00:34:26,920 --> 00:34:29,480
Speaker 1: to have a fulfillment center in every major city if

683
00:34:29,520 --> 00:34:33,279
Speaker 1: you're going to do that kind of business. Right. Um, So,

684
00:34:33,400 --> 00:34:35,400
Speaker 1: Dave Clark is the guy who kind of oversees the

685
00:34:35,400 --> 00:34:38,759
Speaker 1: pivot from we have our warehouse systems set up in

686
00:34:38,880 --> 00:34:41,600
Speaker 1: order to maximize the amount of stuff that we can

687
00:34:41,640 --> 00:34:44,719
Speaker 1: hold and ship out and minimize the costs too. We

688
00:34:44,760 --> 00:34:47,479
Speaker 1: don't care about minimizing the costs anymore. We care about

689
00:34:47,480 --> 00:34:50,160
Speaker 1: getting as many fulfillment centers started as possible so that

690
00:34:50,200 --> 00:34:53,160
Speaker 1: we can get products to people in a day or two. Right. Um,

691
00:34:53,239 --> 00:34:55,799
Speaker 1: And that's again it's become such of like Amazon's two

692
00:34:55,840 --> 00:34:57,560
Speaker 1: day shipping has become such a part of just like

693
00:34:57,680 --> 00:35:01,480
Speaker 1: life now that it it's it's not until like right

694
00:35:01,520 --> 00:35:04,200
Speaker 1: around when the Trump years start that like they actually

695
00:35:04,200 --> 00:35:06,480
Speaker 1: get this really off the ground. You know, it's fairly

696
00:35:06,560 --> 00:35:08,919
Speaker 1: recent that this has been an aspect of modern life

697
00:35:09,080 --> 00:35:13,719
Speaker 1: that you could expect two days shipping on your Amazon products. Um,

698
00:35:13,760 --> 00:35:16,239
Speaker 1: it's it's just kind of taken over everything because it's

699
00:35:16,320 --> 00:35:21,799
Speaker 1: it's offers such close to day. Yeah, I've had ship

700
00:35:21,960 --> 00:35:24,720
Speaker 1: arrived like same day that I didn't even like try

701
00:35:24,800 --> 00:35:34,919
Speaker 1: to order same day. Yeah yeah, yeah, same, It just happened. Yeah. Now,

702
00:35:35,000 --> 00:35:38,320
Speaker 1: over the next five years, Amazon adds a hundred fulfillment

703
00:35:38,320 --> 00:35:41,600
Speaker 1: centers worldwide, and this is where most of their gargantuan

704
00:35:41,800 --> 00:35:44,879
Speaker 1: like huge warehouses and like that are in every city

705
00:35:45,040 --> 00:35:47,439
Speaker 1: kind of or at least on the outskirts of every city.

706
00:35:47,480 --> 00:35:49,280
Speaker 1: This is where all that comes from. So Dave Clark's

707
00:35:49,280 --> 00:35:53,320
Speaker 1: the guy who's like expanding massively the fulfillment center system

708
00:35:53,400 --> 00:35:55,640
Speaker 1: so that they can do this now. At the same time,

709
00:35:55,680 --> 00:35:59,400
Speaker 1: Amazon they realized they can't rely on like ups or

710
00:35:59,520 --> 00:36:03,080
Speaker 1: usp us, so they get their own like fleet of planes. Um.

711
00:36:03,239 --> 00:36:05,319
Speaker 1: But they don't. So one of the things Amazon does

712
00:36:05,360 --> 00:36:07,680
Speaker 1: with its warehouses and with everything is they never want

713
00:36:07,680 --> 00:36:10,279
Speaker 1: to interface with unions in any way, shape or form. Ever.

714
00:36:10,400 --> 00:36:12,920
Speaker 1: They never want to, like, in any way deal with them.

715
00:36:12,960 --> 00:36:15,520
Speaker 1: This is a problem with getting your own planes because

716
00:36:15,560 --> 00:36:21,160
Speaker 1: pilots are unionized, right. Um. So Amazon basically leases planes

717
00:36:21,239 --> 00:36:24,000
Speaker 1: from a company that actually runs everything and hires the

718
00:36:24,000 --> 00:36:26,360
Speaker 1: pilots so that they don't have to deal directly with

719
00:36:26,480 --> 00:36:30,440
Speaker 1: unionized pilots. Um. And they structure their distribution network very

720
00:36:30,440 --> 00:36:34,480
Speaker 1: carefully to avoid any unionized warehouse workers. This also allows

721
00:36:34,520 --> 00:36:37,320
Speaker 1: them to avoid scrutiny over the hours worked or miles

722
00:36:37,400 --> 00:36:40,760
Speaker 1: driven by people in distribution. When this starts killing people

723
00:36:40,760 --> 00:36:43,799
Speaker 1: because drivers are involved in crashes, they run people down

724
00:36:43,800 --> 00:36:47,000
Speaker 1: on the side of people like are dying in the warehouses. Um.

725
00:36:47,080 --> 00:36:48,759
Speaker 1: You can find a bunch of stories about this, and

726
00:36:48,800 --> 00:36:50,839
Speaker 1: when those stories start to come out that there's like

727
00:36:51,360 --> 00:36:56,240
Speaker 1: drivers crashing and killing people trying to make Amazon's like metrics,

728
00:36:56,280 --> 00:36:59,239
Speaker 1: there's backlash um, you know, both from the media, from

729
00:36:59,239 --> 00:37:02,680
Speaker 1: public outcry, and from some people inside of Amazon itself.

730
00:37:03,239 --> 00:37:06,080
Speaker 1: One of these people is Mark Onetto. Mark was head

731
00:37:06,120 --> 00:37:09,000
Speaker 1: of operations in the mid Auts, and he introduced a

732
00:37:09,080 --> 00:37:11,680
Speaker 1: number of innovations to try and make fulfillment centers run

733
00:37:11,719 --> 00:37:14,400
Speaker 1: more smoothly. He was also very concerned with the treatment

734
00:37:14,440 --> 00:37:17,120
Speaker 1: of employees in these fulfillment centers, and he tried to

735
00:37:17,200 --> 00:37:21,600
Speaker 1: force empathy for employees onto Jeffrey Bezos quote from Amazon

736
00:37:21,640 --> 00:37:26,120
Speaker 1: on Bound. In two thousand nine, Onetto's human resources deputy

737
00:37:26,200 --> 00:37:29,480
Speaker 1: David knee Kirk wrote a paper titled Respect for People

738
00:37:29,600 --> 00:37:31,680
Speaker 1: and presented it at an S team meeting. The S

739
00:37:31,719 --> 00:37:34,840
Speaker 1: team is like Jeff and his his like trusted advisors,

740
00:37:34,920 --> 00:37:38,680
Speaker 1: right like it's the top of the Amazon Empire. The paper, yeah,

741
00:37:38,719 --> 00:37:43,280
Speaker 1: the steam. The paper drew from Toyota's proven lean ideology

742
00:37:43,320 --> 00:37:47,080
Speaker 1: and argued for treating people fairly, building mutual trust between

743
00:37:47,080 --> 00:37:50,960
Speaker 1: managers and associates, and empowering leaders to inspire employees rather

744
00:37:51,000 --> 00:37:54,759
Speaker 1: than act as disciplinarians. Bezos hated it. He not only

745
00:37:54,840 --> 00:37:56,719
Speaker 1: railed against it in the meeting, but he called knee

746
00:37:56,760 --> 00:37:59,880
Speaker 1: Kirk the following morning to continue the brow beating. Amazon

747
00:38:00,000 --> 00:38:02,280
Speaker 1: should never imply that it didn't have respect for people

748
00:38:02,280 --> 00:38:04,719
Speaker 1: embedded in the very fabric of how it operated, he said.

749
00:38:05,080 --> 00:38:07,800
Speaker 1: Bezos also solemnly declared that one of the biggest threats

750
00:38:07,800 --> 00:38:10,799
Speaker 1: of the company was a disgruntled and entrenched hourly workforce,

751
00:38:11,040 --> 00:38:14,240
Speaker 1: like the unionized workers that impaired US automakers with strikes

752
00:38:14,280 --> 00:38:18,759
Speaker 1: and onerous contract negotiations. Amazon later denied that Bezos said this.

753
00:38:19,200 --> 00:38:21,920
Speaker 1: He encouraged nie Kirk and Noetto to focus on ensuring

754
00:38:21,960 --> 00:38:24,759
Speaker 1: that FC workers who weren't advancing with an with an

755
00:38:24,760 --> 00:38:27,440
Speaker 1: Amazon stayed for a maximum of three years. He like

756
00:38:27,560 --> 00:38:30,040
Speaker 1: cancels bonuses makes it impossible for well to get raises

757
00:38:30,080 --> 00:38:33,839
Speaker 1: past a certain point, like, rather than we shouldn't be

758
00:38:34,360 --> 00:38:37,920
Speaker 1: enabling the self respect of our fulfillment center workers, we

759
00:38:37,920 --> 00:38:40,040
Speaker 1: should make sure there's as much turnover as possible so

760
00:38:40,080 --> 00:38:42,760
Speaker 1: that nobody's there very longs that no kind of culture conform,

761
00:38:43,000 --> 00:38:46,319
Speaker 1: so that people don't get to identify themselves as part

762
00:38:46,320 --> 00:38:50,520
Speaker 1: of a community of labors and unionized. So Onetto is

763
00:38:50,560 --> 00:38:54,040
Speaker 1: pushing against this, Bezos pushes back, and Nonetto's end at

764
00:38:54,040 --> 00:38:56,480
Speaker 1: the company comes in two thousand eleven, when a local

765
00:38:56,520 --> 00:39:00,040
Speaker 1: Pennsylvania paper reports that an Amazon warehouse near al In

766
00:39:00,120 --> 00:39:03,080
Speaker 1: Town had gotten so hot during the summer that workers

767
00:39:03,080 --> 00:39:05,800
Speaker 1: had passed out and been taken to hospitals. And Amazon

768
00:39:05,840 --> 00:39:07,920
Speaker 1: had been so aware of the danger that they had

769
00:39:08,160 --> 00:39:11,359
Speaker 1: ambulances just waiting outside for when people passed out. Right

770
00:39:11,400 --> 00:39:12,960
Speaker 1: like that, This was like there were yeah, of course

771
00:39:13,120 --> 00:39:15,120
Speaker 1: X number of employees are going to pass out in

772
00:39:15,160 --> 00:39:18,040
Speaker 1: our unair conditioned warehouses in the dead of summer, and

773
00:39:18,040 --> 00:39:21,720
Speaker 1: we'll just keep ambulances on site. There was in my research.

774
00:39:21,800 --> 00:39:24,240
Speaker 1: I actually spoke to someone that spoke to the workers

775
00:39:24,239 --> 00:39:27,160
Speaker 1: there and they were saying, uh, in the offices when

776
00:39:27,160 --> 00:39:30,400
Speaker 1: people went into complain, they had the air con on

777
00:39:30,520 --> 00:39:33,120
Speaker 1: full blast in the office, but in the who house

778
00:39:33,560 --> 00:39:38,040
Speaker 1: they had nothing like yep, so grim. And here's the thing.

779
00:39:38,360 --> 00:39:41,400
Speaker 1: Ohnetto had seen this coming, and in fact, months earlier

780
00:39:41,480 --> 00:39:44,280
Speaker 1: he'd presented a white paper to Bezos another top brass

781
00:39:44,320 --> 00:39:47,280
Speaker 1: about rooftop air conditioners and FC facilities and said they

782
00:39:47,320 --> 00:39:49,319
Speaker 1: were they would cost like fifty two million dollars, but

783
00:39:49,320 --> 00:39:50,799
Speaker 1: he was like, we need to do this. It's going

784
00:39:50,840 --> 00:39:53,560
Speaker 1: to be necessary both for maintaining productivity and taking care

785
00:39:53,600 --> 00:39:57,120
Speaker 1: of our people. Bezos himself shut down the request to

786
00:39:57,160 --> 00:40:00,400
Speaker 1: put in air conditioners now. When the story goes viral

787
00:40:00,440 --> 00:40:03,799
Speaker 1: that workers are passing out, he immediately approves air conditioners

788
00:40:03,800 --> 00:40:06,120
Speaker 1: for the FCS, Like right, it's this kind of thing

789
00:40:06,120 --> 00:40:08,440
Speaker 1: where he's like, no, we're not going to spend that money. Oh,

790
00:40:08,480 --> 00:40:10,800
Speaker 1: now people are yelling at us. Now it's bad PR.

791
00:40:10,920 --> 00:40:14,360
Speaker 1: We'll do it now. And then after he's forced to

792
00:40:14,360 --> 00:40:17,359
Speaker 1: like put in air conditioners, he attacks Onetto for not

793
00:40:17,520 --> 00:40:19,440
Speaker 1: stopping the disaster. The guy who had been like, we

794
00:40:19,480 --> 00:40:21,920
Speaker 1: have to do this and Jeff had said no. Jeff

795
00:40:21,920 --> 00:40:24,120
Speaker 1: goes after him and blames him for the fact that

796
00:40:24,120 --> 00:40:27,240
Speaker 1: there's bad PR and I'm gonna quote from Amazon unbound

797
00:40:27,239 --> 00:40:30,440
Speaker 1: and like how he does this? Fuming a Netto prepared

798
00:40:30,480 --> 00:40:33,680
Speaker 1: to remind Bezos of his original proposal. Colleagues begged him

799
00:40:33,680 --> 00:40:36,399
Speaker 1: to let it go, but he couldn't, as they anticipated.

800
00:40:36,440 --> 00:40:38,719
Speaker 1: The meeting did not go well. Bezos said that as

801
00:40:38,760 --> 00:40:40,960
Speaker 1: a matter of fact, he did remember the paper and

802
00:40:40,960 --> 00:40:43,360
Speaker 1: that it was so poorly written an ambiguous that no

803
00:40:43,360 --> 00:40:46,000
Speaker 1: one had understood what course of a of action Onetto

804
00:40:46,080 --> 00:40:49,480
Speaker 1: was recommending. As other s team members cringed, Bezos declared

805
00:40:49,520 --> 00:40:51,839
Speaker 1: that the entire incident was evidence of what happens when

806
00:40:51,880 --> 00:40:55,160
Speaker 1: Amazon puts people in top jobs who can't articulate their

807
00:40:55,200 --> 00:40:58,279
Speaker 1: ideas clearly and support them with data. And I think

808
00:40:58,320 --> 00:41:01,960
Speaker 1: it's because the actual data, like Jeff was willing to accept,

809
00:41:01,960 --> 00:41:04,840
Speaker 1: oh we'll lose ex productivity in order to save this

810
00:41:05,040 --> 00:41:07,760
Speaker 1: amount of money by not air conditioning our factory or fcs,

811
00:41:07,840 --> 00:41:11,040
Speaker 1: it's worth it netto was saying like, it's bad to

812
00:41:11,080 --> 00:41:13,160
Speaker 1: do this to people. And the thing that Jeff's data

813
00:41:13,200 --> 00:41:15,880
Speaker 1: didn't account for is that like, well, it may not

814
00:41:15,920 --> 00:41:18,719
Speaker 1: have cost you money, but it did cost you bad

815
00:41:18,800 --> 00:41:21,200
Speaker 1: pr when it came out that you were doing this

816
00:41:21,320 --> 00:41:23,880
Speaker 1: to your workers and that hurt your bottom line. But

817
00:41:24,000 --> 00:41:26,279
Speaker 1: no data that Otto could have presented would ever have

818
00:41:26,360 --> 00:41:28,520
Speaker 1: made that case right. There was no like white paper

819
00:41:28,560 --> 00:41:30,080
Speaker 1: saying this is how much money will lose. When The

820
00:41:30,080 --> 00:41:32,080
Speaker 1: New York Times writes a story about this, you know,

821
00:41:32,719 --> 00:41:36,480
Speaker 1: um yeah, yeah, I still don't think he's fully grossed that.

822
00:41:37,320 --> 00:41:39,640
Speaker 1: It's like, you know, he's made more money than he's

823
00:41:39,640 --> 00:41:43,920
Speaker 1: ever made throughout the COVID and the union workers, you know,

824
00:41:44,120 --> 00:41:47,319
Speaker 1: doing the most efforts right now to unionize, and he's

825
00:41:47,360 --> 00:41:50,080
Speaker 1: still just spending millions and millions trying to stop them.

826
00:41:50,120 --> 00:41:51,719
Speaker 1: You know what I mean, rather than just being like, yeah,

827
00:41:51,719 --> 00:41:54,960
Speaker 1: you know what, we made loads and loads of money. Okay,

828
00:41:55,040 --> 00:41:57,279
Speaker 1: let's let's put it back in, let's actually fix this,

829
00:41:57,400 --> 00:42:00,759
Speaker 1: and it's just no, he's just just not having it. Yeah.

830
00:42:00,800 --> 00:42:02,720
Speaker 1: It's kind of like you could you split one percent

831
00:42:02,760 --> 00:42:05,759
Speaker 1: of Amazon profits among FC workers and suddenly you've got

832
00:42:05,760 --> 00:42:09,160
Speaker 1: aware a work force that's like a lot more mode.

833
00:42:09,200 --> 00:42:11,359
Speaker 1: You would have much more career people, you'd have less

834
00:42:11,360 --> 00:42:13,880
Speaker 1: turn up. But whatever, I know, I'm not a businessman. Uh.

835
00:42:13,920 --> 00:42:17,400
Speaker 1: It would also be at least less unethical. So the

836
00:42:17,480 --> 00:42:20,239
Speaker 1: guy who Atto leaves after this, like he quits I

837
00:42:20,239 --> 00:42:22,440
Speaker 1: think the very next year. Um, and the guy who

838
00:42:22,480 --> 00:42:25,080
Speaker 1: takes over from anetto spearheads a bid to acquire a

839
00:42:25,080 --> 00:42:28,480
Speaker 1: company called Kiva. Kiva is a robotics startup. They make

840
00:42:28,520 --> 00:42:32,759
Speaker 1: machines that can transport merchandise containers around warehouses and streamline

841
00:42:32,800 --> 00:42:36,800
Speaker 1: the whole process. Before Kiva, Amazon pickers had walked upwards

842
00:42:36,800 --> 00:42:39,080
Speaker 1: of twelve miles a day to grab products and take

843
00:42:39,120 --> 00:42:41,600
Speaker 1: them to shipping. Kiva bots are able to kind of

844
00:42:41,640 --> 00:42:44,320
Speaker 1: like move all of the shipping containers and these warehouses

845
00:42:44,320 --> 00:42:47,160
Speaker 1: around in order to make things more efficient, so that

846
00:42:47,239 --> 00:42:50,439
Speaker 1: workers have to walk less and can pack more per

847
00:42:50,520 --> 00:42:53,520
Speaker 1: like our um. And these robots really start to hit

848
00:42:53,560 --> 00:42:56,799
Speaker 1: their stride and warehouses around two fourteen. They increase the

849
00:42:56,800 --> 00:42:59,480
Speaker 1: amount of products per square foot and an Amazon FC

850
00:42:59,560 --> 00:43:02,239
Speaker 1: by five deeper scent because it's just so much more efficient.

851
00:43:02,520 --> 00:43:06,360
Speaker 1: But they come with downsides to primarily for the human workers. Quote.

852
00:43:06,960 --> 00:43:10,520
Speaker 1: The robots also transformed labor that was physically exhausting characterized

853
00:43:10,520 --> 00:43:13,800
Speaker 1: by endless walking into work that was instead mentally straining,

854
00:43:13,840 --> 00:43:16,880
Speaker 1: with employees standing in placement not and listeningly repeating the

855
00:43:16,920 --> 00:43:20,160
Speaker 1: same movements over and over. Report in the Center for

856
00:43:20,200 --> 00:43:23,600
Speaker 1: Investigative Reportings Reveal magazine cited an Ocean letter to Amazon

857
00:43:23,640 --> 00:43:27,240
Speaker 1: that said the robots exposed employees to ergonomic risk factors,

858
00:43:27,280 --> 00:43:29,960
Speaker 1: including stress from repeated movements and standing up for ten

859
00:43:30,000 --> 00:43:32,960
Speaker 1: hours a day. Just as the tyrannical invisible force of

860
00:43:33,000 --> 00:43:36,640
Speaker 1: software guided the robot swarms, it also monitored worker performance,

861
00:43:36,880 --> 00:43:40,719
Speaker 1: flagging any quantifiable decrease in productivity and subjecting employees to

862
00:43:40,920 --> 00:43:46,799
Speaker 1: performance improvement plans and possible termination. And that at this

863
00:43:46,880 --> 00:43:49,960
Speaker 1: point where where we are when megacorps starts, Jake, this

864
00:43:50,000 --> 00:43:52,359
Speaker 1: is kind of what feeds into all that Like this

865
00:43:52,440 --> 00:43:55,040
Speaker 1: is how this is. You know, it all makes sense, right,

866
00:43:55,120 --> 00:43:59,319
Speaker 1: None of it is a Yeah, the diet thing is

867
00:43:59,360 --> 00:44:04,600
Speaker 1: just like the obsession with data above just humanity basically

868
00:44:04,880 --> 00:44:07,719
Speaker 1: is kind of you know the stage that the you

869
00:44:07,760 --> 00:44:09,440
Speaker 1: know that all of this is kind of dancing on

870
00:44:09,520 --> 00:44:11,600
Speaker 1: you know what I mean, And it's just it's it's

871
00:44:12,280 --> 00:44:17,799
Speaker 1: it's like it's it's almost like unintentional horrors is just

872
00:44:17,840 --> 00:44:19,880
Speaker 1: like yeah, like just just not even caring, you know

873
00:44:19,880 --> 00:44:22,160
Speaker 1: what I mean, It's like whatever, whatever. The data doesn't

874
00:44:22,160 --> 00:44:24,520
Speaker 1: show that. It's just like me, these are human beings

875
00:44:24,520 --> 00:44:26,839
Speaker 1: with families and lives. It got you know what I mean,

876
00:44:26,920 --> 00:44:30,240
Speaker 1: got to eat, but the data doesn't it doesn't show

877
00:44:30,600 --> 00:44:33,719
Speaker 1: that that level of kind of context. Um, it's just

878
00:44:33,800 --> 00:44:36,760
Speaker 1: kind of sad, you know what I mean. Yeah, yeah,

879
00:44:36,960 --> 00:44:42,880
Speaker 1: it is kind of sad. But that's the that's the

880
00:44:43,160 --> 00:44:47,959
Speaker 1: that's the podcast, that's the episode. Yeah, where you're going

881
00:44:48,000 --> 00:44:53,240
Speaker 1: over it all. It's very jolly. Yeah, it's fun everybody.

882
00:44:53,400 --> 00:44:57,280
Speaker 1: So you know, consider this part one and consider listening

883
00:44:57,280 --> 00:44:59,680
Speaker 1: to Megacorps to get the full story. Now you know

884
00:44:59,719 --> 00:45:03,760
Speaker 1: who f Bezos is, it's time to learn in detail

885
00:45:03,920 --> 00:45:09,000
Speaker 1: every funked up thing his company has done. Um, Yeah, definitely.

886
00:45:09,040 --> 00:45:12,000
Speaker 1: And you know, I would say it's not exactly a

887
00:45:12,040 --> 00:45:13,600
Speaker 1: lot of people have been like, oh it's it's not

888
00:45:13,600 --> 00:45:17,000
Speaker 1: exactly the most uplifting podcast. It's like, well, no, what

889
00:45:17,000 --> 00:45:20,200
Speaker 1: did you think was going to happen? This is about scandals.

890
00:45:20,560 --> 00:45:23,600
Speaker 1: You know, one of the arguably the biggest kind of

891
00:45:24,120 --> 00:45:26,799
Speaker 1: company on earth in a way. So yeah, no it's not.

892
00:45:26,880 --> 00:45:28,399
Speaker 1: But like I said in the kind of in the

893
00:45:28,440 --> 00:45:30,960
Speaker 1: in the intro to the thing, Um, you know, if

894
00:45:31,000 --> 00:45:34,160
Speaker 1: you if you have ever bought anything from Amazon, I

895
00:45:34,200 --> 00:45:36,359
Speaker 1: think you should at least have to be aware of

896
00:45:36,400 --> 00:45:39,359
Speaker 1: the way they operate. And it's not just about the work,

897
00:45:39,400 --> 00:45:41,640
Speaker 1: because we're going to be going into like the surveillance,

898
00:45:41,880 --> 00:45:43,920
Speaker 1: the deals they did with them I six and the

899
00:45:43,920 --> 00:45:47,520
Speaker 1: c I A. Like, there's so many layers to it. Absolutely,

900
00:45:47,840 --> 00:45:51,200
Speaker 1: and uh yeah, check out Megacorp, Jake, do you have

901
00:45:51,200 --> 00:45:54,319
Speaker 1: anything else you want to plug? Um? Yeah, I mean,

902
00:45:54,320 --> 00:45:56,440
Speaker 1: if anyone wants to just check out my work, just

903
00:45:56,480 --> 00:45:59,759
Speaker 1: goes to Jake Hanrahan dot com. Um and you just

904
00:45:59,760 --> 00:46:02,959
Speaker 1: see all my links or whatever. Yeah, alright, check out

905
00:46:03,320 --> 00:46:07,600
Speaker 1: Jake Hanrahand, check out Popular Front, check out Megacorp, and

906
00:46:07,920 --> 00:46:10,840
Speaker 1: check out of the Internet now and go do something offline.

907
00:46:11,320 --> 00:46:16,040
Speaker 1: You've you've spent enough time online today. Yeah, all right,

908
00:46:16,680 --> 00:46:17,440
Speaker 1: bam