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--> YouTube-Generated Transcript <--
0:01 · Amazon laid off 30,000 people this week
0:03 · in a move that had been widely
0:05 · telegraphed for months. But the real
0:07 · story is not about AI and jobs. The
0:11 · story I'm seeing over and over and over
0:13 · again in the news media is, hey, AI is
0:16 · automating all of these jobs. This is
0:18 · why we're seeing this. We're going to
0:20 · see more of these cuts. The media seems
0:22 · really excited about that story and they
0:24 · just want to keep telling it. And I got
0:26 · to say it keeps not being in this case.
0:30 · There's a very interesting reason why
0:32 · it's incorrect. And I think it's
0:34 · actually really important for us to
0:35 · understand because it gets at a core
0:37 · narrative that a lot of people have
0:39 · about AI that is wrong. So what actually
0:42 · happened here? Number one, you need to
0:44 · understand where Amazon's business came
0:46 · from. I spent half a decade at Amazon.
0:47 · I'm very fluent in this. Amazon makes
0:50 · their money on AWS, not on retail. The
0:53 · whole store doesn't do anything, right?
0:54 · that the margins on the store are
0:56 · ridiculously low. They make no money.
0:58 · They would not be a profitable company.
0:59 · Jeff Bezos would not be wandering around
1:01 · in a yacht. Amazon makes their money on
1:03 · AWS, which means the entire street, all
1:06 · of Wall Street checks AWS's growth
1:09 · numbers every single year obsessively to
1:12 · tell whether or not Amazon is doing
1:15 · well. And the problem is that AWS's
1:18 · growth numbers have been declining for
1:21 · the last few years. And so they're down
1:23 · to 18% growth year-over-year, which is a
1:25 · deceleration in their last quarterly
1:27 · report. And the street doesn't like it.
1:30 · And part of the reason the street
1:31 · doesn't like it is that in the meantime,
1:34 · AWS's two main rivals, Google Cloud and
1:38 · Microsoft Azure, have been catching up
1:40 · and they have been doing so with AI.
1:43 · Right? If you think about where to go
1:45 · for AI, you think about Azure and you
1:47 · think about Google Cloud. And I got to
1:49 · be honest, AWS is a distant third. They
1:53 · just aren't there. And so the challenge
1:55 · for AWS is they need to show that they
1:58 · are still serious players in the AI era.
2:02 · To do that, what do you need? You need a
2:04 · specific piece of hardware that Jensen
2:06 · Fuang sells that nobody else has. It's
2:08 · the Nvidia GPU. I well there's a few
2:10 · others that have GPUs but by and large
2:13 · it's all Jensen's GPUs and you got to
2:15 · buy a bunch of them and they don't come
2:18 · cheap like think of this as a computer
2:20 · chip that is worth a car and you have to
2:22 · buy thousands and thousands and tens and
2:24 · thousands of them to get anywhere with
2:26 · AI especially at scale especially if
2:28 · you're serving corporations that is the
2:31 · dilemma that AWS faces now look at it
2:34 · from a corporate finance perspective you
2:36 · have to do that without damaging your
2:39 · margins in AWS because the whole reason
2:42 · Amazon is worth anything as a company is
2:45 · because of AWS's margin. So you don't
2:48 · dare damage AWS's margins to get this
2:51 · job done, but you have to buy a whole
2:54 · bunch of GPUs. In finance terms, you
2:57 · have to add a ton to your capital
3:00 · expenditures, your capex. Well, if
3:03 · you're going to do that and you want to
3:04 · keep your margins consistent, you have
3:06 · to cut other places that are in your
3:08 · expenses category. You have to look at
3:10 · other fixed expenses. And what is the
3:12 · biggest fixed expense category that you
3:14 · have? It is salaries. It's salaries.
3:17 · That is what they're looking at. And so
3:19 · when you have that tradeoff, what you
3:21 · should really be looking at is Amazon
3:24 · did not fire 30,000 people because AI
3:28 · automation was already taking their
3:30 · jobs. Amazon fired 30,000 people because
3:34 · they needed the money today in order to
3:37 · buy GPUs to desperately try to secure a
3:41 · place in the future of AI cloud. That is
3:44 · the actual story. Now, that story does
3:47 · not sound as nice for Amazon as a future
3:49 · forward story about how we're automating
3:51 · with AI. So, the story Amazon's putting
3:54 · out is, hey, we're automating with AI,
3:57 · so we don't need these jobs anymore. No.
3:59 · Especially as someone who worked there,
4:01 · the interior workflows at Amazon, and
4:04 · anyone will tell you this, this is not
4:05 · proprietary. It's all duct tape and
4:07 · bailing wire in there. Like, everyone
4:09 · does a lot of manual stuff. And that's
4:11 · been very intentional as Amazon has
4:13 · grown because it helps us keep costs low
4:15 · for customers. And so, there is not a
4:17 · huge massive automation magical solution
4:21 · that they have invented that allows them
4:23 · to right now cut 30,000 jobs. It just
4:25 · that's not how it works. The people who
4:28 · remain are very stressed. They may have
4:30 · ambitious projects to eventually bring
4:32 · AI into those areas. But if I'm looking
4:34 · at it at a very high level, what I see
4:37 · is not an investment roadmap for AI
4:40 · automation that is already paid off that
4:43 · they're trying to show. No, what I see
4:46 · is Amazon saying these are areas where
4:48 · we can afford to take a risk on less
4:51 · talent getting less done. In other
4:52 · words, these are areas that we can
4:54 · divest a little bit. And that's
4:55 · interesting because that makes a whole
4:57 · lot of sense when you look at where some
4:59 · of these cuts happened. As an example,
5:01 · MGM got hit, the Hollywood studio that
5:04 · Amazon bought a few years ago. I got to
5:06 · say, if you're asking yourself, is this
5:08 · an area where we have already invested
5:10 · our best efforts to automate AI right
5:13 · away? MGM would not be at the top of my
5:15 · list. I do not think that is the most
5:17 · strategic place in Amazon where Amazon
5:20 · would have poured vast resources to
5:22 · invest in AI. No. But I do think it
5:25 · makes a ton of sense as a place where
5:28 · Amazon would say we can invest less for
5:30 · a little bit and cut talent for a bit
5:32 · because we desperately need to
5:34 · reallocate cash over to the GPU side of
5:38 · the business. Well, that makes a lot of
5:40 · sense, doesn't it? And people aren't
5:42 · reporting that. And here's the reason
5:43 · why all of this matters. The narrative
5:46 · out there is very simple. We are in a
5:49 · bubble. Everywhere I look, we are in a
5:50 · bubble. AI is a bubble. AI is a bubble.
5:52 · But step back, take a breath, don't just
5:55 · obsess over the news. If we are in a
5:58 · bubble, why does Amazon have a 25%
6:03 · essentially overage rate? Right? The the
6:05 · the amount of demand they have for for
6:07 · GPUs right now vastly exceeds the
6:10 · available GPUs. Would that be true if we
6:13 · were in a bubble? Demand is a sign that
6:17 · we are not in a bubble. surging
6:19 · corporate demand that Azure has trouble
6:21 · meeting because Azure's expanded their
6:23 · data center investments this year that
6:25 · Google Cloud has trouble meeting that
6:27 · Amazon has tons of trouble meeting is a
6:30 · sign that we have built something with
6:33 · AI that is valuable enough that
6:36 · corporations are lining up like crazy to
6:38 · buy it. If you have customers out the
6:41 · door and cannot serve enough chips to
6:44 · all of them, that is a sign that you are
6:47 · not in a bubble. Ipso facto. By
6:50 · definition, it is a sign that you are
6:52 · not in a bubble. And I don't understand
6:54 · why this is so hard for people. I don't
6:57 · understand why journalists are so
6:59 · interested in pedalling the narrative
7:01 · that AI has already automated jobs and
7:04 · that somehow we are simultaneously in a
7:05 · bubble because both of those things
7:07 · cannot be true at once. If we lived in a
7:10 · world where AI had magically automated
7:12 · away all jobs or whatever they're
7:14 · claiming, well then it wouldn't be a
7:16 · bubble because people would like
7:18 · corporations would have demand for that,
7:19 · right? They they would ask for that to
7:21 · happen so they could expand their
7:22 · footprint. maybe not even to fire people
7:24 · but to add more like c talent and
7:26 · capability right there'd be demand for
7:27 · it if we were you know in a bubble we
7:30 · would not have the kind of surging
7:34 · interest in AI at every level small
7:36 · medium business uh commercial scale
7:38 · enterprise scale the consumer like all
7:41 · of us as individuals that's not the
7:43 · story we see and yet we are being asked
7:46 · to believe by the media simultaneously
7:49 · that AI is so big and scary it is
7:51 · automating jobs and also though that
7:53 · somehow we are magically in a bubble and
7:54 · it's all fake. It's it's not both
7:56 · people. It's not both. And in this case,
7:58 · it's not either one of them because we
8:00 · don't have the talent yet to build AI
8:03 · systems that fully automate roles and we
8:06 · like not for a while. Like it's not
8:08 · there. And yet corporations love that
8:10 · narrative because it makes them future
8:12 · focused. It makes Wall Street happy
8:13 · because Wall Street doesn't know what AI
8:15 · is. And everybody like goes away happy
8:17 · with the story. And nobody pays
8:19 · attention to the contradictions here.
8:20 · The the answer is very simple. They just
8:22 · need to buy GPUs because corporations
8:25 · need to buy cloud compute. And that is
8:26 · what happened. And none of that makes
8:28 · getting fired easier. I don't want to
8:30 · sort of pretend that being able to
8:31 · explain it makes it better. It it sucks
8:33 · to be fired. I've been fired before. It
8:35 · just it's terrible. And so for those of
8:38 · you who have unwillingly left Amazon,
8:40 · there are Amazon alumni. We're out here.
8:43 · We look out for each other. We're doing
8:44 · our best. And you will find a spot. And
8:46 · I know that none of this makes it
8:48 · better. But I do hope that we actually
8:50 · can tell the truth about what is going
8:51 · on instead of getting fooled by stories
8:54 · that are not even not even coherent,
8:56 · right? Like you can't have both an AI
8:59 · bubble and AI automating all jobs. It
9:01 · does not work. And yet that is the story
9:02 · we're being sold. So there you go.
9:04 · That's the truth. That's why it matters.
9:06 · And that's why we need to pay attention
9:07 · when people make layoff announcements
9:08 · like that. Good luck. Don't conf.

--> YouTube-Generated Transcript <--
0:01	·	Amazon laid off 30,000 people this week
0:03	·	in a move that had been widely
0:05	·	telegraphed for months. But the real
0:07	·	story is not about AI and jobs. The
0:11	·	story I'm seeing over and over and over
0:13	·	again in the news media is, hey, AI is
0:16	·	automating all of these jobs. This is
0:18	·	why we're seeing this. We're going to
0:20	·	see more of these cuts. The media seems
0:22	·	really excited about that story and they
0:24	·	just want to keep telling it. And I got
0:26	·	to say it keeps not being in this case.
0:30	·	There's a very interesting reason why
0:32	·	it's incorrect. And I think it's
0:34	·	actually really important for us to
0:35	·	understand because it gets at a core
0:37	·	narrative that a lot of people have
0:39	·	about AI that is wrong. So what actually
0:42	·	happened here? Number one, you need to
0:44	·	understand where Amazon's business came
0:46	·	from. I spent half a decade at Amazon.
0:47	·	I'm very fluent in this. Amazon makes
0:50	·	their money on AWS, not on retail. The
0:53	·	whole store doesn't do anything, right?
0:54	·	that the margins on the store are
0:56	·	ridiculously low. They make no money.
0:58	·	They would not be a profitable company.
0:59	·	Jeff Bezos would not be wandering around
1:01	·	in a yacht. Amazon makes their money on
1:03	·	AWS, which means the entire street, all
1:06	·	of Wall Street checks AWS's growth
1:09	·	numbers every single year obsessively to
1:12	·	tell whether or not Amazon is doing
1:15	·	well. And the problem is that AWS's
1:18	·	growth numbers have been declining for
1:21	·	the last few years. And so they're down
1:23	·	to 18% growth year-over-year, which is a
1:25	·	deceleration in their last quarterly
1:27	·	report. And the street doesn't like it.
1:30	·	And part of the reason the street
1:31	·	doesn't like it is that in the meantime,
1:34	·	AWS's two main rivals, Google Cloud and
1:38	·	Microsoft Azure, have been catching up
1:40	·	and they have been doing so with AI.
1:43	·	Right? If you think about where to go
1:45	·	for AI, you think about Azure and you
1:47	·	think about Google Cloud. And I got to
1:49	·	be honest, AWS is a distant third. They
1:53	·	just aren't there. And so the challenge
1:55	·	for AWS is they need to show that they
1:58	·	are still serious players in the AI era.
2:02	·	To do that, what do you need? You need a
2:04	·	specific piece of hardware that Jensen
2:06	·	Fuang sells that nobody else has. It's
2:08	·	the Nvidia GPU. I well there's a few
2:10	·	others that have GPUs but by and large
2:13	·	it's all Jensen's GPUs and you got to
2:15	·	buy a bunch of them and they don't come
2:18	·	cheap like think of this as a computer
2:20	·	chip that is worth a car and you have to
2:22	·	buy thousands and thousands and tens and
2:24	·	thousands of them to get anywhere with
2:26	·	AI especially at scale especially if
2:28	·	you're serving corporations that is the
2:31	·	dilemma that AWS faces now look at it
2:34	·	from a corporate finance perspective you
2:36	·	have to do that without damaging your
2:39	·	margins in AWS because the whole reason
2:42	·	Amazon is worth anything as a company is
2:45	·	because of AWS's margin. So you don't
2:48	·	dare damage AWS's margins to get this
2:51	·	job done, but you have to buy a whole
2:54	·	bunch of GPUs. In finance terms, you
2:57	·	have to add a ton to your capital
3:00	·	expenditures, your capex. Well, if
3:03	·	you're going to do that and you want to
3:04	·	keep your margins consistent, you have
3:06	·	to cut other places that are in your
3:08	·	expenses category. You have to look at
3:10	·	other fixed expenses. And what is the
3:12	·	biggest fixed expense category that you
3:14	·	have? It is salaries. It's salaries.
3:17	·	That is what they're looking at. And so
3:19	·	when you have that tradeoff, what you
3:21	·	should really be looking at is Amazon
3:24	·	did not fire 30,000 people because AI
3:28	·	automation was already taking their
3:30	·	jobs. Amazon fired 30,000 people because
3:34	·	they needed the money today in order to
3:37	·	buy GPUs to desperately try to secure a
3:41	·	place in the future of AI cloud. That is
3:44	·	the actual story. Now, that story does
3:47	·	not sound as nice for Amazon as a future
3:49	·	forward story about how we're automating
3:51	·	with AI. So, the story Amazon's putting
3:54	·	out is, hey, we're automating with AI,
3:57	·	so we don't need these jobs anymore. No.
3:59	·	Especially as someone who worked there,
4:01	·	the interior workflows at Amazon, and
4:04	·	anyone will tell you this, this is not
4:05	·	proprietary. It's all duct tape and
4:07	·	bailing wire in there. Like, everyone
4:09	·	does a lot of manual stuff. And that's
4:11	·	been very intentional as Amazon has
4:13	·	grown because it helps us keep costs low
4:15	·	for customers. And so, there is not a
4:17	·	huge massive automation magical solution
4:21	·	that they have invented that allows them
4:23	·	to right now cut 30,000 jobs. It just
4:25	·	that's not how it works. The people who
4:28	·	remain are very stressed. They may have
4:30	·	ambitious projects to eventually bring
4:32	·	AI into those areas. But if I'm looking
4:34	·	at it at a very high level, what I see
4:37	·	is not an investment roadmap for AI
4:40	·	automation that is already paid off that
4:43	·	they're trying to show. No, what I see
4:46	·	is Amazon saying these are areas where
4:48	·	we can afford to take a risk on less
4:51	·	talent getting less done. In other
4:52	·	words, these are areas that we can
4:54	·	divest a little bit. And that's
4:55	·	interesting because that makes a whole
4:57	·	lot of sense when you look at where some
4:59	·	of these cuts happened. As an example,
5:01	·	MGM got hit, the Hollywood studio that
5:04	·	Amazon bought a few years ago. I got to
5:06	·	say, if you're asking yourself, is this
5:08	·	an area where we have already invested
5:10	·	our best efforts to automate AI right
5:13	·	away? MGM would not be at the top of my
5:15	·	list. I do not think that is the most
5:17	·	strategic place in Amazon where Amazon
5:20	·	would have poured vast resources to
5:22	·	invest in AI. No. But I do think it
5:25	·	makes a ton of sense as a place where
5:28	·	Amazon would say we can invest less for
5:30	·	a little bit and cut talent for a bit
5:32	·	because we desperately need to
5:34	·	reallocate cash over to the GPU side of
5:38	·	the business. Well, that makes a lot of
5:40	·	sense, doesn't it? And people aren't
5:42	·	reporting that. And here's the reason
5:43	·	why all of this matters. The narrative
5:46	·	out there is very simple. We are in a
5:49	·	bubble. Everywhere I look, we are in a
5:50	·	bubble. AI is a bubble. AI is a bubble.
5:52	·	But step back, take a breath, don't just
5:55	·	obsess over the news. If we are in a
5:58	·	bubble, why does Amazon have a 25%
6:03	·	essentially overage rate? Right? The the
6:05	·	the amount of demand they have for for
6:07	·	GPUs right now vastly exceeds the
6:10	·	available GPUs. Would that be true if we
6:13	·	were in a bubble? Demand is a sign that
6:17	·	we are not in a bubble. surging
6:19	·	corporate demand that Azure has trouble
6:21	·	meeting because Azure's expanded their
6:23	·	data center investments this year that
6:25	·	Google Cloud has trouble meeting that
6:27	·	Amazon has tons of trouble meeting is a
6:30	·	sign that we have built something with
6:33	·	AI that is valuable enough that
6:36	·	corporations are lining up like crazy to
6:38	·	buy it. If you have customers out the
6:41	·	door and cannot serve enough chips to
6:44	·	all of them, that is a sign that you are
6:47	·	not in a bubble. Ipso facto. By
6:50	·	definition, it is a sign that you are
6:52	·	not in a bubble. And I don't understand
6:54	·	why this is so hard for people. I don't
6:57	·	understand why journalists are so
6:59	·	interested in pedalling the narrative
7:01	·	that AI has already automated jobs and
7:04	·	that somehow we are simultaneously in a
7:05	·	bubble because both of those things
7:07	·	cannot be true at once. If we lived in a
7:10	·	world where AI had magically automated
7:12	·	away all jobs or whatever they're
7:14	·	claiming, well then it wouldn't be a
7:16	·	bubble because people would like
7:18	·	corporations would have demand for that,
7:19	·	right? They they would ask for that to
7:21	·	happen so they could expand their
7:22	·	footprint. maybe not even to fire people
7:24	·	but to add more like c talent and
7:26	·	capability right there'd be demand for
7:27	·	it if we were you know in a bubble we
7:30	·	would not have the kind of surging
7:34	·	interest in AI at every level small
7:36	·	medium business uh commercial scale
7:38	·	enterprise scale the consumer like all
7:41	·	of us as individuals that's not the
7:43	·	story we see and yet we are being asked
7:46	·	to believe by the media simultaneously
7:49	·	that AI is so big and scary it is
7:51	·	automating jobs and also though that
7:53	·	somehow we are magically in a bubble and
7:54	·	it's all fake. It's it's not both
7:56	·	people. It's not both. And in this case,
7:58	·	it's not either one of them because we
8:00	·	don't have the talent yet to build AI
8:03	·	systems that fully automate roles and we
8:06	·	like not for a while. Like it's not
8:08	·	there. And yet corporations love that
8:10	·	narrative because it makes them future
8:12	·	focused. It makes Wall Street happy
8:13	·	because Wall Street doesn't know what AI
8:15	·	is. And everybody like goes away happy
8:17	·	with the story. And nobody pays
8:19	·	attention to the contradictions here.
8:20	·	The the answer is very simple. They just
8:22	·	need to buy GPUs because corporations
8:25	·	need to buy cloud compute. And that is
8:26	·	what happened. And none of that makes
8:28	·	getting fired easier. I don't want to
8:30	·	sort of pretend that being able to
8:31	·	explain it makes it better. It it sucks
8:33	·	to be fired. I've been fired before. It
8:35	·	just it's terrible. And so for those of
8:38	·	you who have unwillingly left Amazon,
8:40	·	there are Amazon alumni. We're out here.
8:43	·	We look out for each other. We're doing
8:44	·	our best. And you will find a spot. And
8:46	·	I know that none of this makes it
8:48	·	better. But I do hope that we actually
8:50	·	can tell the truth about what is going
8:51	·	on instead of getting fooled by stories
8:54	·	that are not even not even coherent,
8:56	·	right? Like you can't have both an AI
8:59	·	bubble and AI automating all jobs. It
9:01	·	does not work. And yet that is the story
9:02	·	we're being sold. So there you go.
9:04	·	That's the truth. That's why it matters.
9:06	·	And that's why we need to pay attention
9:07	·	when people make layoff announcements
9:08	·	like that. Good luck. Don't conf.