Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.
Speaker 1: I'm Joe Wisenthal and I'm Tracy Alloway. Tracy, have I
Speaker 1: ever told you like my idea for like a two
Speaker 1: part podcast or like a series of two part podcasts?
Speaker 1: A series of two parts? Is it the debate one? No,
Speaker 1: it's different. Okay, So I don't know about you, But
Speaker 1: most of the time, after we do these interviews, I
Speaker 1: usually have questions that I started kicked myself for not
Speaker 1: having asked oh, yes, yes. So this often happens on
Speaker 1: the podcast because we often touch on kind of wide
Speaker 1: varying topics and we're not experts in a lot of
Speaker 1: the things that we talked about, and often the first
Speaker 1: episode is sort of you get to know your subject matter,
Speaker 1: and then you leave it with even more questions. Yeah.
Speaker 1: So I often kicked myself, like, oh, I should have
Speaker 1: I should have obviously asked that obviously. And then the
Speaker 1: other thing that happens is you post then the episode
Speaker 1: comes out, and then like people on Twitter and elsewhere,
Speaker 1: they'll talk about it. They talked about it, they're like,
Speaker 1: what I'm curious about is actually like, oh, that's really
Speaker 1: good question too. I should have thought of that. So,
Speaker 1: like I thought, like a thing that we should do
Speaker 1: maybe one day is schedule, like, have all episodes be
Speaker 1: two parters where we do an interview with a guest,
Speaker 1: take a week, sort of magrinate on it, think about it,
Speaker 1: what are some questions we wish we would ask, and
Speaker 1: then have the second episode schedule. I really like that idea.
Speaker 1: It's sort of like it's almost the octopus model of
Speaker 1: podcast episodes, where like one one episode just springs forth
Speaker 1: a dozen you arms and legs that you can talk
Speaker 1: about forever. So today we're kind of gonna we're kind
Speaker 1: of gonna be doing that. We're this is I think
Speaker 1: we might be This might be close. I'm not sure
Speaker 1: what the record is, but this might be close to
Speaker 1: the soonest we've ever had a guest on so soon
Speaker 1: after they appeared the show. This is a pilot for
Speaker 1: podcast two parters. The only other time I can think
Speaker 1: of is we talked to Claudia Sam twice before the
Speaker 1: pandemic and was because it was sort of like her
Speaker 1: general views on how to like forecast recessions, and then
Speaker 1: like two weeks later, like the pandemic was in view,
Speaker 1: and it's like, oh, I shoot, this might be really bad,
Speaker 1: so he had her on really fast. But this is
Speaker 1: going to be close to that record. I think, yeah,
Speaker 1: sounds good. All right, let's do it. So we recently
Speaker 1: talked to Patrick McKenzie. He is a technology infrastructure financial
Speaker 1: infrastructure specialist. He worked for Stripe for six years. He's
Speaker 1: currently an advisor. He's the author of the Bits about
Speaker 1: Money newsletter, and we talked to him about like corporate
Speaker 1: I why it is the way it is? Why does
Speaker 1: it seem to be like years and years behind what
Speaker 1: we think of the cutting edge of software wise it
Speaker 1: off and clunky wise it off and have these big
Speaker 1: technical issues that can take a while to fix. That
Speaker 1: was a great conversation. The public loved it. But there's
Speaker 1: a lot going on in software these days. And the
Speaker 1: other sort of big trend that we haven't really talked
Speaker 1: about is that for the first time and I don't know,
Speaker 1: maybe like fifteen years, we've been seeing all these tech layoffs, right,
Speaker 1: And I think there was a little bit of tension
Speaker 1: in that episode in that we were talking about why
Speaker 1: corporate software is so bad. So it's almost like, well,
Speaker 1: obviously there's a need for better software, and yet all
Speaker 1: these big tech companies that ostensibly provide these services are
Speaker 1: laying people off, but also in the broader macro picture.
Speaker 1: Since we recorded that episode, we had a payrolls report
Speaker 1: that came out much, much stronger than anyone expected. And
Speaker 1: yet we've seen these big tech companies layoff people. And
Speaker 1: so the question obviously becomes, is this something specifically about
Speaker 1: tech or are these layoffs sort of the first sign
Speaker 1: of something broader to come in the economy. And the
Speaker 1: other thing that everyone points out to when when you
Speaker 1: see these announcements from Meta and Alphabet, at various startups
Speaker 1: and Microsoft and Amazon they all done it is like
Speaker 1: they edited so many jobs over the last two years
Speaker 1: that actually these layoffs are like fairly small in uh,
Speaker 1: the grand scheme of things, even for these companies, based
Speaker 1: on the amount of hiring that they've done. Yeah, but
Speaker 1: I saw I saw a figure from Goldman Sex I
Speaker 1: think it was Yannatsis, and he was talking about in
Speaker 1: the tech sector, most of the companies that have been
Speaker 1: laying people off grew their head count by over since
Speaker 1: the pandemic basically because they thought all the pandemic trends
Speaker 1: were going to keep going. Maybe there's a little bit
Speaker 1: of labor hoarding. But it's a big figure. Absolutely. You know,
Speaker 1: people in our industry, journalism, when journalists lose their jobs,
Speaker 1: there's always trolls on Twitter saying, oh, learn to code.
Speaker 1: You know, that's like a thing. I think at one
Speaker 1: point even like Twitter start banning people for saying that.
Speaker 1: But I guess the question is, right now, when you
Speaker 1: see these layoffs, like should we learn to code or
Speaker 1: is that not you know, is that not the career
Speaker 1: safety that that it used to be? So all kinds
Speaker 1: of questions about what is going on in the market
Speaker 1: for tech talent. I'm sure someone's going to tell us
Speaker 1: to learn to code. For the record, I can code
Speaker 1: just in like very non useful languages. I have no well,
Speaker 1: I coded in basic like yeah, yeah exactly, like C
Speaker 1: plus plus and like some really basic HTML. But all right,
Speaker 1: let's talk to someone who knows more about this question
Speaker 1: than we do. We're bringing back Patrick. Thank you so
Speaker 1: much for coming back on the podcast. Thanks very much
Speaker 1: for having me. Absolutely so. I guess the question is,
Speaker 1: before we even start talking about the recently off announcements,
Speaker 1: why don't we start with like the hiring boom that
Speaker 1: we really saw over the last two years, just massive
Speaker 1: amount of headcount added and all these companies, we know
Speaker 1: who they are, what drove that. So how about we
Speaker 1: rule back history to two thousand nineteen and if you're
Speaker 1: looking at recent history, as of two thousand nineteen, tech
Speaker 1: has been on sort of an uninterrupted series of a
Speaker 1: bunch of very good years, broad based expansion across the
Speaker 1: entire industry, basically writing continuing to ride the wave that
Speaker 1: had happened since the late pots. Is that how we
Speaker 1: was it in English with the consolidation of mobile gains, etcetera, etcetera.
Speaker 1: Then the pandemic happened and there was a brief pause
Speaker 1: of okay, is this going to be an absolutely catastrophic
Speaker 1: event for the entire world economy. Many bad things happened
Speaker 1: during the pandemic, the way it played out for tech
Speaker 1: was probably not how anyone would have expected one. There
Speaker 1: was sort of a one two punch of a combined
Speaker 1: fiscal response from governments built in the United States and
Speaker 1: worldwide to stave off a huge economic disaster, which had
Speaker 1: the effect of both putting money into consumers pockets and
Speaker 1: also using the markets for assets, for example tech stocks,
Speaker 1: which will come back to the importance so that in
Speaker 1: a moment to a lot of the customers were, due
Speaker 1: to various non pharmaceutical inventions, sitting at home with very
Speaker 1: little to do other than use the internet. And so
Speaker 1: a lot of commerce that had been possible on the
Speaker 1: internet before the share of it that was soaked up
Speaker 1: by the Internet in uh both sort of like semi
Speaker 1: discretionary places like food delivery, but also much less discretionary
Speaker 1: places like you know, core supermarkets suddenly shifted online in
Speaker 1: a very very fast way. And so this combination of
Speaker 1: there's more money slashing around and more of it is
Speaker 1: falling into the online bucket led to absolutely blockbuster years
Speaker 1: for tech companies, and it was a real like trying
Speaker 1: to keep your fingers onto the rocket internally at the companies,
Speaker 1: like the amount of new users that was on boarding,
Speaker 1: at the rate of growth of the business, the raw
Speaker 1: volumes of stuff that was going through the pipes made
Speaker 1: it like difficult to keep everything up and running. And
Speaker 1: a good news front, the business is largely successfully did
Speaker 1: keep up and running during a time where society very
Speaker 1: much needed them to. They also started to like readjust
Speaker 1: their projections of what the future would look like, and
Speaker 1: for a while it was looking like. Yeah. The phrase
Speaker 1: that was going around was decades of growth were happening
Speaker 1: every couple of weeks in terms of, you know, our
Speaker 1: anticipated long term shift of the offline economy into the
Speaker 1: online economy. And there was a big question of how
Speaker 1: long does that continue for and is that pulling forth
Speaker 1: growth growth that is happening in the future, is it
Speaker 1: a one time spike, etcetera, etcetera. To to various structural
Speaker 1: and competitive dynamics, a lot of firms spit simultaneously. This
Speaker 1: is a pretty durable change. We find ourselves crushed by
Speaker 1: the amount of demand we're seeing right now. We're going
Speaker 1: to need to hire and hire aggressively to deal with
Speaker 1: this and to position ourselves for what we see as
Speaker 1: the you know, eventual coming out of the pandemic future.
Speaker 1: And as a result of this, companies were mature companies
Speaker 1: the Google's Amazons Facebooks of the world. We're hiring on
Speaker 1: the order of like year over year growth across large
Speaker 1: portions of their business. Somewhat earlier stage companies, companies that
Speaker 1: might look like a stripe even though stripe as uh
Speaker 1: somewhat larger these days, or early stage startups were on
Speaker 1: boarding multiples of their pre pandemic had count as we
Speaker 1: used over the course of the pandemic, so huge expansion
Speaker 1: during the during the interval, and then as we came
Speaker 1: out of the pandemic, companies assessed a number of things.
Speaker 1: One the growth rates tended to go back to his
Speaker 1: historical norms rather than this shot in the arm that
Speaker 1: the pandemic was offering. Importantly, and you know, tech is
Speaker 1: a wide sector. It touches every part of the economy
Speaker 1: these days, so it's difficult to say with huge generalizations,
Speaker 1: but as top line level things did not decline back
Speaker 1: to two thousand nineteen. And again, two thousand nineteen was
Speaker 1: not a bad year for tech. It was a, you know,
Speaker 1: a pretty good year after a number of pretty good years.
Speaker 1: So we haven't gone back to the pre pandemic baseline.
Speaker 1: We haven't even stopped growing. In a number of cases,
Speaker 1: the growth curve has just spent downwards, and so the
Speaker 1: sustained like plus plus headcount growth over time didn't look
Speaker 1: like it could be that could be sustained. And then
Speaker 1: companies started to look at things that they had allowed
Speaker 1: to happen for the course of the pandemic to characterize
Speaker 1: these broadly. One of the things that happened during the
Speaker 1: pandemic was due to the lockdowns and advisability of having
Speaker 1: large numbers of people congregate in small pockets of air,
Speaker 1: a bunch of companies went to both remote work and
Speaker 1: remote hiring where they might not have had a huge
Speaker 1: amount of institutional experience with that model of working before,
Speaker 1: and after two to three years of working with these
Speaker 1: newer cohorts of people, they've found that there are some
Speaker 1: practices that they want to continue from this room at
Speaker 1: work world into the future, and that there's some amount
Speaker 1: of internal impetus to return to office and have sort
Speaker 1: of a cultural reset around the office or headquarters, etcetera
Speaker 1: as the sort of beating center of these firms. I've
Speaker 1: worked remote for most of my career, myself of a
Speaker 1: broad fan of the model. Let's say that there was
Speaker 1: some cultural tension and companies on where the locus of
Speaker 1: activity is going to be, whether it's going to be
Speaker 1: in this online in zoom meetings and slack all the time,
Speaker 1: or in the office high band with communication directly with
Speaker 1: trusted peers, and a lot of companies wanted to have
Speaker 1: a bit of a pullback towards the office, and then
Speaker 1: they're looking more granularly at the classes of people to
Speaker 1: the heart over the last couple of years, and found
Speaker 1: that in comparison to prior classes, there was a bit
Speaker 1: of cultural drift relative to where the companies want their
Speaker 1: baselines to be, and also in some cases a bit
Speaker 1: of a measured productivity difference versus where they wanted their
Speaker 1: baselines to be. That's sort of expected because when you're
Speaker 1: pulling out all the steps to hire, you like necessarily
Speaker 1: you have to be a little less choosy than you
Speaker 1: normally are. You have, you know, to the extent that
Speaker 1: you describe any value at all to the in person
Speaker 1: interview loop, which I describe relatively little value too, but
Speaker 1: hopefully like slightly greater than zero. You you lose that
Speaker 1: amount of signal and they're sort of hiring in a
Speaker 1: slightly more challenge fashion than usual. And so I think
Speaker 1: that companies will be pretty quiet about saying, but we'll
Speaker 1: we'll say to themselves is we probably have a few
Speaker 1: more regrets in like hiring classes than we did in
Speaker 1: the hiring classes as a percentage, Patrick, this actually leads
Speaker 1: to something that I want to ask you, but what
Speaker 1: does blow actually look like in the tech sector, and
Speaker 1: you know, is it something that only emerges as business
Speaker 1: activity actually slows down, or even in one would you
Speaker 1: have characterized tech as bloated. So it's difficult. This tech
Speaker 1: sort of subsumes more and more of the economy into
Speaker 1: its every increasing embrace to make like huge paint with
Speaker 1: raw brush assertions across all of it. But let's see
Speaker 1: where to start here. So one, the number of things
Speaker 1: that are done in these large companies are extremely varied.
Speaker 1: People might have a image that like most people who
Speaker 1: could google our engineers, that's actually not the case. Depending
Speaker 1: on the company we're talking about between twenty and the
Speaker 1: people who work at the company are technologists broadly written,
Speaker 1: they are software engineers, their system administrators, their designers at
Speaker 1: some companies reporting to the same division. And then the
Speaker 1: rest are every sort of worker that you would have
Speaker 1: in any company in the economy. Lawyers, regulatory people, customer
Speaker 1: sport agents, etcetera, etcetera, etcetera. Management layers upon layers of management.
Speaker 1: So what does what does company growth look like? In
Speaker 1: one case, it is staffing up more teams to work
Speaker 1: on products that already exist. Sometimes staffing teams that sort
Speaker 1: of like grow with the the rate of usage of
Speaker 1: your products. So like customer service teams typically grow relatively
Speaker 1: linearly with the usage of your service. Sometimes it's teams
Speaker 1: that grow relatively nearly with the size of your organizations.
Speaker 1: So as companies were having these sort of like unprecedented
Speaker 1: amounts of employees getting on boarded every year, they needed
Speaker 1: larger recruiting divisions to staff up there are other employees,
Speaker 1: and it's just based on like the productivity math of
Speaker 1: a recruiter. And you can, like finger to the wind
Speaker 1: that if you hire a recruiter, that recruiter will be
Speaker 1: able to hire twenty five people in the year. And
Speaker 1: so if you need to hire four thousand people, then
Speaker 1: you know, work math backwards, you require a hundred sixty
Speaker 1: recruiters that you didn't have previously. That will tend to
Speaker 1: cause your recruiting division to get larger as you are
Speaker 1: doing a rapid expansion, and then it will contract faster
Speaker 1: than the rest of your company will when you decide
Speaker 1: to take your foot off the gas bottle. So those
Speaker 1: are the the things that are sort of less inside
Speaker 1: of your control. You you just need to keep doing
Speaker 1: them to run the business, and then you're making some
Speaker 1: more speculative investments on like what are what is our
Speaker 1: new product line up going to look? Like? What features
Speaker 1: are we going to add? And so the basic unit
Speaker 1: of organization within an engineering organization these days is a
Speaker 1: single engineering team will typically be like five to eight people,
Speaker 1: and that team has a mental bandwidth to deal with
Speaker 1: three relatively narrowly scoped problems. And so the more that
Speaker 1: you want your software services suite, etcetera. To do, the
Speaker 1: more like narrowly scoped problems that come into its domain,
Speaker 1: more like five to eight people engineering teams you need,
Speaker 1: and so you might find yourself in a position where
Speaker 1: you've hired like five to eight people to work on
Speaker 1: three relatively narrowly scoped problems somewhat opportunistically, and then when
Speaker 1: you you know, come to three and are thinking very
Speaker 1: rigorously around like, Okay, we think we're a little bigger
Speaker 1: than we were when we were efficient back a couple
Speaker 1: of years ago. We think the economic environment not be
Speaker 1: might not be as strong and as we were model link,
Speaker 1: Which of all the problems in our company are the
Speaker 1: ones that we definitely need to keep focusing on, and
Speaker 1: which can we refer into later or just our corridor
Speaker 1: business right now, then perhaps like some of these nearroly
Speaker 1: scoped problems are not at the top of our list.
Speaker 1: And then if you consider, you know, like this product
Speaker 1: that we thought we would bring to market in three
Speaker 1: maybe it will not be brought to mark till then
Speaker 1: there might be like ten teams implicated by that that
Speaker 1: you do not have propletied for I have a lot
Speaker 1: of questions. You know, when Ellen bought Twitter, and he,
Speaker 1: like what you know, much more aggressive with the layoffs
Speaker 1: than anything else that we've seen. There were all these
Speaker 1: like vcs and stuff. A common to the Dirty Secret
Speaker 1: and Silicon Valley is that all these companies could do
Speaker 1: that they have fifty of their employees not really working
Speaker 1: on anything and not really contributing anything. And like, thank
Speaker 1: you Ellen for showing that this could be done. And
Speaker 1: Twitter still is operating. Although I don't know how the
Speaker 1: businesses or whether he cut too deep to the bone
Speaker 1: or whatever, but like, would you hear that, like is
Speaker 1: that the case that just like over the years, setting
Speaker 1: aside the unrealistic expectations of one and maybe two, was
Speaker 1: there just a wide scale over hiring relative to the
Speaker 1: needs of the business. So tech has been in sort
Speaker 1: of a land grab mode for essentially all of my
Speaker 1: adult life. We certainly haven't hit the asom to oute
Speaker 1: of how many things in the economy can be orchestrated
Speaker 1: by software. We certainly haven't hit the assom tote of
Speaker 1: how many human and human interactions will be intermediated by
Speaker 1: a technical system happening over a smartphone, etcetera, etcetera. In
Speaker 1: that sort of land grab mode, you aren't simply like
Speaker 1: trying to answer what is the minimal set of things
Speaker 1: we can do with the minimal number of people, but
Speaker 1: are sort of opportunistically looking at what are the next
Speaker 1: ten things that we can try such that one of
Speaker 1: them becomes a company defining product feature, etcetera, etcetera. I
Speaker 1: have a little bit of risk reflective contrarianism when people
Speaker 1: say all tech companies are overstaffed by Could you cut
Speaker 1: eighty percent of people who work at tech companies and
Speaker 1: still have something functional at the end of the day.
Speaker 1: Probably true, that would be extremely painful. But if you
Speaker 1: went into a very different mode of operation and just
Speaker 1: wanted them to continue the products and services they had
Speaker 1: three years ago, possibly that could be done. Probably wouldn't
Speaker 1: be optimal for any of them. That's one major reason
Speaker 1: why nobody does it. There's also some not gone like cultural, etcetera.
Speaker 1: Effects that make it virtually unthinkable. If you were an
Speaker 1: executive at at a tech company and you were sufficiently
Speaker 1: in your cups and had a had a heart to
Speaker 1: heart with someone and said, what's the true number of
Speaker 1: Like if I could wave a magic wand and no consequences,
Speaker 1: where would our staffing be? Would probably be like eighty
Speaker 1: five to ninetent of what it is currently. I think
Speaker 1: I think most people would say, like, oh, there's a
Speaker 1: bit of there's a bit of like I hate the
Speaker 1: word fat in this context about you know, a little
Speaker 1: bit of fluff around the edges, but we're not in
Speaker 1: systemically a terrible place. And I think you know you
Speaker 1: you would get different numbers from different people in different
Speaker 1: parts of the organization, but that feels like plus or
Speaker 1: minus right to me. Should be noted that I was
Speaker 1: a beer worker b rather than the sort of executive
Speaker 1: that would be tesked with making that kind of decision. Patrick,
Speaker 1: you mentioned the sort of impetus towards creating company defining features,
Speaker 1: and this is also something I've always wondered, is there
Speaker 1: a bias in tech towards creating new products and our
Speaker 1: employees and engineers you know, rewarded for doing new things
Speaker 1: rather than maybe maintaining the old ones and perfecting those. Oh,
Speaker 1: this is an extremely important thing to understand and the
Speaker 1: behavior of the large tech companies from outside of them
Speaker 1: that they all have what's called a PERF process in
Speaker 1: the industry, it's called PURF outside is a performance review,
Speaker 1: and the performance reviews are largely how a company takes
Speaker 1: creative work that is done over this time scale of
Speaker 1: like quarters and years, and it is often sort of
Speaker 1: indevigable and very area and reduces it to a number
Speaker 1: such that the company can dole out things of value
Speaker 1: like promotions and bonuses and career paths, etcetera, etcetera. And
Speaker 1: PERF happens on a semi annual or annual basis, and
Speaker 1: the way it PERF works that most large tech companies
Speaker 1: is heavily biases in the direction of getting your name
Speaker 1: attached to new things that shipped in the world versus
Speaker 1: you know, I was assigned to this legacy product, the
Speaker 1: product did not go down for six months, you should
Speaker 1: definitely give me a bonus on that basis. Oddly enough,
Speaker 1: this is not straightforwardly the things that is in the
Speaker 1: company's interests because all of the money is made by existing, well,
Speaker 1: not all the money. The supermajority of money in the
Speaker 1: tech companies is made by satisfying customers you already have
Speaker 1: rather than getting new customers, and the supermajority of money
Speaker 1: is made on your oldest and tourist products rather than
Speaker 1: the new stuff. But institutionally, tech companies biased towards we
Speaker 1: want our best people to be on the new things
Speaker 1: all of the time. And if your individual best people
Speaker 1: want to be, you know, doing the hard yards that
Speaker 1: keeps the old stuff running, they will quickly be dissuaded
Speaker 1: by their mentors and managers, etcetera, and say don't, no, no,
Speaker 1: that is not the way to exceed expectations. You will like,
Speaker 1: if you only do great maintenance work for the for
Speaker 1: the next couple of years, you will be, you know,
Speaker 1: severely career limited here. So figure out something new to
Speaker 1: do and make sure your name is attached to it
Speaker 1: in a way that is legible to your manager and
Speaker 1: your manager's manager and this performance roview process. So let's
Speaker 1: talk about the layoffs that we've seen. Because you said
Speaker 1: something interesting in your first answer, which is that's sort
Speaker 1: of like hiring discipline, hiring quality. During those crazy years
Speaker 1: of one part of may have been loose, maybe not
Speaker 1: as the standards were a little lower, or maybe people
Speaker 1: just didn't fit or something like that. When companies these
Speaker 1: days are now or recently making the decisions about who
Speaker 1: they're going to let go, how skewed is it towards
Speaker 1: that sort of recent cohort. Because the other thing I
Speaker 1: could see is that look at many companies, you probably
Speaker 1: have people who have been there forever who are getting
Speaker 1: paid extremely high salaries or very good salaries just based
Speaker 1: on the fact that they got some bump every single year.
Speaker 1: Maybe they're not pulling their weight to some perceived degree
Speaker 1: as much as they used to be. So how much
Speaker 1: of the you know, when when are they when the
Speaker 1: executive look and say, okay, we're gonna make cuts. How
Speaker 1: much was it skewed towards the new cohort versus seeing
Speaker 1: as like this is an opportunity to get rid of
Speaker 1: some highly paid employees maybe don't add as much value
Speaker 1: as they want. So a disclaimer off the top, layoffs
Speaker 1: are like understandably traumatic for the people who go through them.
Speaker 1: I don't want to minimize that. At the same time,
Speaker 1: I think we often, particularly as as workers in this industry,
Speaker 1: sort of like advocate responsibility for understanding the like structures
Speaker 1: that cause these things to happen in ways that are
Speaker 1: not in our interests. So broadly it's it's good to
Speaker 1: have like open conversations about how these sort of decisions
Speaker 1: are made. I think it is different on a firm
Speaker 1: differing basis, but broadly speaking, you would not want your
Speaker 1: simply to like roll back for the last six months
Speaker 1: of your hiring. There's a couple of different reasons for that.
Speaker 1: One is that when you're dealing with these complex ecosystems
Speaker 1: that sufficiently large companies ecosystem to itself, there's all sorts
Speaker 1: of levers that you are like managing a parallel and
Speaker 1: one of those levers is that you are attempting to
Speaker 1: balance the seniority ranges in various parts of your organization
Speaker 1: such that you always have a mixed within some error
Speaker 1: bars of how many people that you have they're acclimating
Speaker 1: to the company versus how many who have acclimated and
Speaker 1: could do productive work, versus how many are in that
Speaker 1: senior mode where they can lately parachuting to consult on
Speaker 1: things and do the architecture stuff that you're more intermediate
Speaker 1: employees might not be able to do. Yet, if you
Speaker 1: sort of create a bubble in the pipeline by concentrating
Speaker 1: your cuts in the people that were only hired in
Speaker 1: the last six months to two years, then you are
Speaker 1: setting yourself up for a bubble a couple of years
Speaker 1: from now where you have far too few people at
Speaker 1: a portion of the experience curve to do work that
Speaker 1: you urgently need to do work on a week by week,
Speaker 1: quarter by quarter basis. And so if you come to
Speaker 1: the conclusion that we've hired a few too many people
Speaker 1: over the last couple of the last couple of months,
Speaker 1: what are we going to do about that? You have
Speaker 1: to distribute your cuts over a larger number of cohorts
Speaker 1: than the most recent cohorts, or you will set yourself
Speaker 1: fun for some pain. There's also some compliance and legal
Speaker 1: issues that come up with is employees and you already
Speaker 1: a protected class in particular jurisdictions, which also plays into
Speaker 1: it into a little bit. But the biggest reason is
Speaker 1: to avoid causing the operational issues for your company layoffs.
Speaker 1: Is performance management that is a thing that exists in
Speaker 1: the world. And so you know, if you were hearing
Speaker 1: skeptical FECs on Twitter, that they would say about large
Speaker 1: software companies is not merely that they were a little
Speaker 1: bit flabby, but that they were a little bit uh
Speaker 1: self assured of their position in the world, and it
Speaker 1: had too many good years in a row. And if
Speaker 1: you got attached to them, you could you get a
Speaker 1: job in a corner office and not do all that
Speaker 1: much and still be fine. I think that is a
Speaker 1: little exaggerated, but let's say there's certainly cases of it,
Speaker 1: and there's certainly some people like mature into a career
Speaker 1: where they continue being impactful over years and decades, and
Speaker 1: some people end up in sort of a tenured professor
Speaker 1: mode where they've become critical to the organization because they
Speaker 1: know a couple of things that the organization needs to know,
Speaker 1: but they don't bring the same intensity that they used
Speaker 1: to in their career. And then there are some people
Speaker 1: who have like successfully created a niche for themselves inside
Speaker 1: the company, but the company might not desire to exist,
Speaker 1: and nobody wakes up in the morning and says today,
Speaker 1: I want to do layoffs. But given a circumstance where
Speaker 1: everyone in the industry is doing layoffs, some executives might say, okay,
Speaker 1: it is a good time to reevaluate and like turn
Speaker 1: up the heat a little bit on our performance management
Speaker 1: and say, okay, is there anyone who has been coasting
Speaker 1: a little too long? Is there anyone who has uh,
Speaker 1: you know, created a secure little nest for themselves in
Speaker 1: a way that that nest does not add a lot
Speaker 1: of value to the company. Given that we we need
Speaker 1: to usher some people on two new positions, let's start
Speaker 1: with that first and then move to the cuts that
Speaker 1: are going to take more mental energy to do. You know,
Speaker 1: we're talking broadly about hiring discipline and the idea of bloat.
Speaker 1: And this is a slightly loaded question, but to what degree,
Speaker 1: if any, do you think the sort of maybe monopolistic
Speaker 1: mode that some big tech companies have built around their
Speaker 1: businesses has contributed to some complacency on the hiring front.
Speaker 1: And a little adverse to the word monopolistic, but I
Speaker 1: think I get what you're getting at, and that there
Speaker 1: is certainly a lot of rent created in the technology
Speaker 1: industry where these are some of the most effective businesses
Speaker 1: ever created in any industry. Google AdWords will print a
Speaker 1: ginormous amount of money next year, and almost no amount
Speaker 1: of action taken by any set of first actors internal
Speaker 1: or external ad to Google will cause Google AdWords to
Speaker 1: not be worth many, many, many billions of dollars, and
Speaker 1: so the margins on it are very high as well
Speaker 1: in comparison to you know, we we're talking last time
Speaker 1: about the airline industry, where the airline industry has struggled
Speaker 1: mightily to maintain like single digit percentage positive margins over
Speaker 1: a multi decade timeframe. Tech doesn't have that problem. The
Speaker 1: nature of these very sticky products that shearsket size of them,
Speaker 1: and the margins do tend to create a little more
Speaker 1: room for that flabbiness than in exists in many industries
Speaker 1: that have more of a cutthroat reputation. This is sort
Speaker 1: of the polar opposite question. But nowadays we hear a
Speaker 1: lot about the possibility of companies hoarding labor when it
Speaker 1: comes to tech. How much of that do you think
Speaker 1: has actually gone on in the sense that do you
Speaker 1: see tech companies opportunistically hiring people just so their competitors
Speaker 1: can't get their hands on them. I've heard this theory
Speaker 1: advanced many times, and honestly, I don't think it is
Speaker 1: very explanatory, and sometimes it's phrased Google would rather hire
Speaker 1: a particular talented engineers so that they don't create a
Speaker 1: startup and then eventually become competition to one of Google's products.
Speaker 1: If hypothetically that were something that actually motivated executives at
Speaker 1: tech companies, there would be a number of things that
Speaker 1: would be easier to do than quote unquote labor hoarding
Speaker 1: that we don't do institutionally. So in finance, there's this
Speaker 1: institution of gardening leave. Tech doesn't institutionalize gardening leave at
Speaker 1: any level almost anywhere in the industry. And if you
Speaker 1: were thinking about let's prevent highly talented people from doing
Speaker 1: interesting things for our competitors or for new startups that
Speaker 1: they could create. The people in the industry that you
Speaker 1: have the like tightest speed on their productivity level are
Speaker 1: your existing employees, and so you would be you would think, oh, well,
Speaker 1: like the natural place to start is like start with
Speaker 1: people who are already work here and say, if you leave,
Speaker 1: we would like to buy twelve months of your time
Speaker 1: sight unseen, and no one does that. And there's other
Speaker 1: things that you can do Broadly tech is there's always
Speaker 1: a bit of push and pull between the needs of
Speaker 1: a company and the needs of employees, but broadly tech
Speaker 1: who is strikingly pro worker relative to many industries in
Speaker 1: the United States. These things that are done that would
Speaker 1: be consistent with the labor hoarding hypothesis just are not done.
Speaker 1: You know, you can talk to the people that are
Speaker 1: involved in the decisions that that are read on the
Speaker 1: outside as labor hoarding, and they never advanced that as
Speaker 1: a reason to you know, buy up. A new company
Speaker 1: that has four engineers attached to it is typically phrased
Speaker 1: something more similar to, well, this is a team that
Speaker 1: seems already jelled. They're clearly highly highly productive individual contributors,
Speaker 1: and we could have a bunch of engineering recruiters work
Speaker 1: for months to find for similarly talented individuals or the
Speaker 1: m and a team can like tick one box off
Speaker 1: in Q one and get them all in the door
Speaker 1: for the price of one low check. Let's do it.
Speaker 1: The notion of like and let's take this team off
Speaker 1: the table, so they don't, you know, have a market
Speaker 1: success in three years and create something competitive with us
Speaker 1: never comes up. Okay, we started talking about why the
Speaker 1: hiring boom happened. In the first place, we've talked about
Speaker 1: maybe some of the decisions on who is getting cut.
Speaker 1: Let's talk about the sort of prospects for the people
Speaker 1: that have lost their jobs and or the people that
Speaker 1: are thinking about going into a career in tech. So
Speaker 1: how quickly do you perceive that the people losing their
Speaker 1: jobs over the last several months are finding new offers? Like,
Speaker 1: let's start really simple. Can I tack something onto that,
Speaker 1: which is how how fungible are these types of jobs
Speaker 1: in reality? Yeah, A long time ago, in a place
Speaker 1: far far away, during the dot com crash, I was
Speaker 1: graduating from university and the Wall Street Journal was which
Speaker 1: read the Wall Street Journal every day with my father
Speaker 1: growing up. It was how I learned to read the
Speaker 1: Wall Street Journal. Could do no no wrong in my
Speaker 1: as as a an undergraded engineer, and the Wall Street
Speaker 1: Journal was pretty decided that yep, engineering as a field
Speaker 1: is done in the United States of America. Henceforth, all
Speaker 1: engineering will happen in Asia. And I said, oh, chucks,
Speaker 1: I really wanted to get an engineering job. I guess
Speaker 1: I have to move to Asia, and so I did.
Speaker 1: Oh back, now we know the origin story. This is
Speaker 1: the backstory of how I ended up spending my entire
Speaker 1: adult life in Japan. Now that ended up being a
Speaker 1: good read like a good life decision for me for
Speaker 1: entirely unrelated reasons. But it turns out there were, in
Speaker 1: fact engineers hired between two thousand and four and two
Speaker 1: thousand and twenty three in the United States, and so
Speaker 1: reports of the field's demise were heavily exaggerated. If you
Speaker 1: are considering a career in engineering, every reason you had
Speaker 1: to consider a career in engineering in is like still
Speaker 1: a reason to do it. So this like minor wobble
Speaker 1: that will be forgotten in a matter of months. Please
Speaker 1: don't allow it to like cause you to make major
Speaker 1: drastic life decisions. Although life is what happens when when
Speaker 1: you're busy dealing with these little wabbles. Okay, so that
Speaker 1: out of the way. How fungible are people? Broadly speaking?
Speaker 1: In the early levels of career, tech tends to cast
Speaker 1: a very wide net and hire people for what's often
Speaker 1: called horsepower, with the expectation that they will be able
Speaker 1: to specialize over time. There is some degree of worry
Speaker 1: that if you spend ten years or fifteen years in
Speaker 1: a particular industry doing. The quote often used is have
Speaker 1: the same year ten years in a row, then you
Speaker 1: will end up over specialized and only be available for
Speaker 1: doing that sort of thing in the future. Depending on
Speaker 1: the thing you are doing, there might be a sharply
Speaker 1: limited set firms for which that is relevant. But broadly speaking,
Speaker 1: the engineers that were hired to do anything in the
Speaker 1: first five seven is years of their career are broadly
Speaker 1: expected to be able to do not quite anything, but
Speaker 1: like a large subset of all the things that a
Speaker 1: tech employer could want an engineer to do. And so
Speaker 1: the liquidity in the tech market within like a broad
Speaker 1: class like recruiters or engineers, etcetera liquidity between job titles,
Speaker 1: exact roles, exact companies business model of the company is
Speaker 1: very high. And I'm forgetting what Joe's original question was, Well,
Speaker 1: so are they finding just a short term like you
Speaker 1: must hear from people, you must talk to people like, uh,
Speaker 1: people just got cut off? Are they recruiters already reaching
Speaker 1: out to them from different companies? Structurally tech company this
Speaker 1: would be a bad pool quote. Structurally tech companies are
Speaker 1: like sharks. Okay, we're gonna we're gonna pull their quote.
Speaker 1: Just just like sharks, like the way that their gills work.
Speaker 1: They have to keep swimming or they stop getting oxygen.
Speaker 1: And that's an unfortunate thing for most creatures. The tech
Speaker 1: companies because of their staffing models, they and that thing
Speaker 1: we talked about earlier, where they are constantly mixing the
Speaker 1: number of people at each level of seniority within the company.
Speaker 1: They have to keep hiring. And so even if an
Speaker 1: individual company decides like, Okay, we're going to like push
Speaker 1: pause for six weeks and do quote unquote hiring freeze
Speaker 1: one the amount of time and they can actually do
Speaker 1: that and not severely damage the business is limited. So
Speaker 1: it pauses always temporary unless the company is going down
Speaker 1: the tubes. And like the large tech companies certainly are
Speaker 1: not going down the tubes. Some startups might get shaken
Speaker 1: out at the march and tow to UH funding constraints,
Speaker 1: et cetera, But the like the overall business of the Internet,
Speaker 1: continues to grow apace. So pauses are temporary nature. And
Speaker 1: there exists, you know, like many different companies inside that
Speaker 1: the broader ambit of tech. Some of them might be
Speaker 1: positive any given moment, Some of them are you know,
Speaker 1: still attempting to make new investments for three and some
Speaker 1: of them while they're not in uh sort of rapid
Speaker 1: growth mode, growth mode for doing things like you know,
Speaker 1: we have to back fill for people who are leaving
Speaker 1: the company, and in a typical year at a typical
Speaker 1: tech company, that may be like ten percent of our
Speaker 1: engineering staff. So if we've got two thousand engineers, we
Speaker 1: have two hundred engineers that we are slate to hire
Speaker 1: in Interestingly, one of the things that cost a bit
Speaker 1: of the over hiring was companies have this model for
Speaker 1: what percentage of people will leave in a year and
Speaker 1: therefore how many you need to hire just to stay
Speaker 1: at the current level of employment that you have. And
Speaker 1: when the economy started wobbling in, what happened was the
Speaker 1: rates of voluntary attrition that companies, meaning that people who
Speaker 1: resigned out of their own volution, went lower than the
Speaker 1: model predicted. And because you need to like set in
Speaker 1: place a process that takes months to hire people, but
Speaker 1: the process of deciding not to quit is not visible
Speaker 1: for those months, that resulted in sort of like a
Speaker 1: hiring overhang, and so companies overshot their targets for how
Speaker 1: many people would be in the company, which doesn't sound
Speaker 1: like an easy thing to do, but it is a
Speaker 1: very easy thing to do if there is a sort
Speaker 1: of like sharp change and employee behavior with regards to
Speaker 1: things that they have total control over and don't have
Speaker 1: to announce to you, like deciding to leave or not
Speaker 1: leave in a statistical fashion. Are there signs that tech
Speaker 1: workers should look out for that they're about to be
Speaker 1: laid off? Like do you stop being a signed new projects?
Speaker 1: Do your access codes get cut off? Does someone come
Speaker 1: take your stapler off your desk? Like? What exactly are
Speaker 1: the warning signs that you might be in the danger zone? Many,
Speaker 1: many tech people have a large degree of stress with
Speaker 1: regards to whether I'm doing well, am I on the list, etcetera, etcetera,
Speaker 1: And I don't want to add to that stress. Broadly.
Speaker 1: You should. You should have an understanding of how performance
Speaker 1: is calculated at your company, and consider that official view
Speaker 1: of your performance to be perhaps more important than you
Speaker 1: would naively believe it too, because the official view where
Speaker 1: you're the entirety of your performance is reduced down to
Speaker 1: like one number, I'm a full or for this six months.
Speaker 1: That is the only view that is going to be
Speaker 1: available to someone who might be two, three, four steps
Speaker 1: above you on the ladder when they're going to make
Speaker 1: hard decisions in a hypothetical future where they're making hard decisions.
Speaker 1: So the things that cause formal visibility to accompany are
Speaker 1: anomalously important, and the career oriented people around you, who
Speaker 1: are very good at work in those systems their advantages
Speaker 1: will find advantages based on that. But I wouldn't, you know,
Speaker 1: over rotate on perfect The only thing we're thinking about
Speaker 1: seems simple, But just do great work and then make
Speaker 1: sure people are aware of the fact that you did
Speaker 1: the great work, and then things will tend to work
Speaker 1: out in a career fashion over only a long period
Speaker 1: of time, not gun. So I have a question that
Speaker 1: bridges this conversation with the conversation we had last week
Speaker 1: about I T. And I realized I should have asked
Speaker 1: it last time. This whole episode has been because it's
Speaker 1: actually I actually only just had one question from last time.
Speaker 1: I had to come up with a whole excuse for
Speaker 1: why we needed to have you back out just so
Speaker 1: I could ask this. But occurred to me, you know,
Speaker 1: like in the in the business press, we're always reporting
Speaker 1: on c E O s getting fired or let go
Speaker 1: and hired. Sometimes CFOs I don't see much coverage of,
Speaker 1: like c t O S or c I O is
Speaker 1: like the people who run the internal tech systems being
Speaker 1: let go for poor performance. I actually think the only
Speaker 1: time I can ever remember hearing any sort of CTO
Speaker 1: or something losing their job for poor performance is probably
Speaker 1: like fifteen years ago when Twitter was always having the
Speaker 1: fail wills and like they weren't scaling very well during
Speaker 1: the boom years. And other than that, I can't actually
Speaker 1: recall a time in which I like read a story
Speaker 1: about it, you know, a CTO being laid off for
Speaker 1: bad performance. How often does that happen? And you know,
Speaker 1: in the context of whether we're talking about tech companies
Speaker 1: or all. You know, I think we were talking about
Speaker 1: Southwest and others last time, Like how often do the
Speaker 1: head of do those positions lose their job because they say, like,
Speaker 1: our I is not good. So it's a complicated subject
Speaker 1: for a number of reasons. One is that the degree
Speaker 1: of saliency of CTO most companies to the media is
Speaker 1: relatively low. The degree of saliency of many things that
Speaker 1: are very important in the tech industry to the media
Speaker 1: is lower than many people in the tech industry, but
Speaker 1: like and that is one cause of the frequent conflict
Speaker 1: between the media and tech. But be that doesn't two
Speaker 1: people get laid off for for performance. Yes, one relatively
Speaker 1: frequent thing that happens relative to the incidents of senior
Speaker 1: senior executives departing is the uh sort of like fall
Speaker 1: on your sword motion if there is a significant outage.
Speaker 1: Is a thing that frequently happens, are frequently relative to
Speaker 1: all causes for a departure and hesitant to give you
Speaker 1: the example because Tokyo is a small town, but there
Speaker 1: are a number of banks, both in Japan and outside
Speaker 1: of Japan that have had disabling computer outages for like
Speaker 1: days to weeks at a time, where that is an
Speaker 1: extremely extremely thing to be avoided for a bank and
Speaker 1: rules up fairly directly to the head of I T
Speaker 1: or the CEO. And there are cases where either the
Speaker 1: head of I T or the CEO of have left
Speaker 1: us a result of doing that. There is one thing
Speaker 1: that I do like about the culture that is Japanese management,
Speaker 1: where in the sort of like ritualized speech that an
Speaker 1: executive gives it that they will often say police don't
Speaker 1: blame the people that had their hands on the keyboards
Speaker 1: during this. The fact that this was allowed to happen
Speaker 1: was a result of managements miss decisions or taken over
Speaker 1: the course of years. I presided over them, and as
Speaker 1: a result this uh, this allage. Even if you know
Speaker 1: it was one person individually fat fingering something that took
Speaker 1: us down for a week, this belongs at my door,
Speaker 1: and I'm resigning to take responsibility for it. There are
Speaker 1: many things I don't love about Japanese management culture, but
Speaker 1: that bit I do like. Another thing is there are
Speaker 1: reasons for companies to be other than other than maximally
Speaker 1: public about the fact that we are removing a senior
Speaker 1: executive for cost. If you remember the over the course
Speaker 1: of the last couple of years, the I T sector
Speaker 1: has been in sort of like massive boom mode. Companies
Speaker 1: are extremely protective of their brand with respect to engineering candidates.
Speaker 1: Nobody wants to join a organization that exists under a
Speaker 1: cloud who's CTO just got fired for being an idiot.
Speaker 1: So the thing that might happen is like, oh, well,
Speaker 1: the previous VP of engineering wasn't quite up to enough.
Speaker 1: Maybe they can be shuffled onto a different project, and
Speaker 1: we're going to hire a CTO above them. If you've
Speaker 1: already hired a CTO, that's a bit of a bit
Speaker 1: of a more difficult thing. But like shuffles with regards
Speaker 1: to who are the most important people in the engineering
Speaker 1: organization and is there a separate product organization? Do they
Speaker 1: report to the same people, etcetera, etcetera, are sometimes caused
Speaker 1: by like X isn't getting it done. We want to
Speaker 1: like shuffle in why, But we don't want that to
Speaker 1: be seen as a repudiation of X. Not because we
Speaker 1: care about X's opinions so much, but we care about
Speaker 1: how this will be read by internal engineers who we
Speaker 1: want to keep attaptioned to the company and external candidas. Alright,
Speaker 1: one last small question they'll probably eject, a question that
Speaker 1: we can talk about for a long time, but just
Speaker 1: real quickly. So the one one area within tech that
Speaker 1: seems like almost certainly going to be hiring like crazy
Speaker 1: for years at this point is anything to do with AI.
Speaker 1: And you know we all know what's going on there.
Speaker 1: How much are the skills that some of these like
Speaker 1: sort of cutting edge AI companies in need of. How
Speaker 1: much are these skills that sort of legacy or existing
Speaker 1: tech workers might have, or how much are the skills
Speaker 1: that they need something that like you really need years
Speaker 1: of like focus training in the specific area to satisfy
Speaker 1: what these companies need. Can I can I add another
Speaker 1: thing onto the back of that place? How many coding
Speaker 1: jobs will something like chat GPT destroy? Ye? Should people
Speaker 1: stop learning to code? Yeah? Yeah, talk about talk about
Speaker 1: So I have a glib but true answer with respect
Speaker 1: to our advanced AI techniques going to destroy programming jobs.
Speaker 1: The first program or class of programs that we had
Speaker 1: where an advanced computer was obviating the need for human
Speaker 1: programmers was called the compiler, where instead of doing you know,
Speaker 1: complex low level and instructions directly and assembly and speaking
Speaker 1: sort of natively the language of the computer, you use
Speaker 1: what we're called high level programming language is like C
Speaker 1: back in the day. And then the compiler would you know,
Speaker 1: use its magic AI powers to turn that C into
Speaker 1: assembly language so that you didn't have to laboriously do
Speaker 1: the assembly language itself. So every technology that gives programmers
Speaker 1: more power, more capability to do things that are valuable
Speaker 1: for human society probably increases the aggregate demand for programmers
Speaker 1: is sort of like my high level view on the
Speaker 1: world and if yet to see a contrary example to that.
Speaker 1: So an interesting question with regards to AI is what
Speaker 1: are the like, what series of steps is going to
Speaker 1: be necessary to take it to market in a way
Speaker 1: that it actually creates the value for individual people land
Speaker 1: for society, and that it seems to have Latin within it.
Speaker 1: And if you look at like chat GPT, if you
Speaker 1: like I view it as an iceberg, there's the above
Speaker 1: the waterline part and below the waterline part, and below
Speaker 1: the waterline part has some let's say deep deep magic
Speaker 1: there bracketting out that magic for the moment, it seems
Speaker 1: like the above the waterline part was very important in
Speaker 1: why everyone has heard chat GPT and probably used it
Speaker 1: if you're listening to this podcast, but it hasn't heard
Speaker 1: of like similar efforts at Google, etcetera. The reason is
Speaker 1: that there was a you know, a product focused team
Speaker 1: that made a relatively pedestrian piece of software like a
Speaker 1: chat interface, but made it really, really good and like
Speaker 1: work on that to the point where people's interactions with
Speaker 1: the underlying large language model would be like sufficiently effervescent
Speaker 1: that it would screenshot that interaction has shared it over
Speaker 1: to Twitter, and so everything that above the water line
Speaker 1: part is amenable to the to the technologies and tactics
Speaker 1: that existing engineers have with no modification whatsoever. There you're
Speaker 1: talking to the back end. The back end is implemented
Speaker 1: in a different kind of magic than your back ends
Speaker 1: usually are. But the back end has always been magic too.
Speaker 1: That is like part of the answer. There's an interesting question,
Speaker 1: like how much of the work is going to be
Speaker 1: that above the waterline part. The productization of these you know,
Speaker 1: creating like new forms of user interfaces, new models for
Speaker 1: interactions with users, new metaphors that we have to teach
Speaker 1: to people, like new you know there there might be
Speaker 1: an entire field and like education and how to do
Speaker 1: I don't know, prompt engineering, well, prompt engineering being how
Speaker 1: do you type in the right series of incantations to
Speaker 1: the machine so that the the spirited some and stuff
Speaker 1: out of the ether does the right thing for you? So, like,
Speaker 1: what percentage of the work will happen there? First? What
Speaker 1: percentage of the work will happen on these like core
Speaker 1: under the hood model things. A sub sub question to
Speaker 1: that too is like okay, so for the work happening
Speaker 1: at that model layer. Is that work going to happen
Speaker 1: at every company that consumes models, or is it going
Speaker 1: to happen primarily at open AI and Google and Microsoft.
Speaker 1: And we can count the number of firms that like
Speaker 1: need this these engineers on a single hand. In a
Speaker 1: world where we count the number of firms that need
Speaker 1: like dedicated hard AI researchers on a single hand, that
Speaker 1: probably implies like lower total employment of them than in
Speaker 1: the world where every firm that touches AI has its
Speaker 1: own AI practice on staff. But it's at least as
Speaker 1: of like the current state play deeply uncertain where that
Speaker 1: will shake out. And so these are some of the
Speaker 1: questions that get debated upon people at both the AI
Speaker 1: firms and also like you know, if if you are
Speaker 1: a VC that's adventure that's investing in the space, you
Speaker 1: are probably having like a number of interesting dinner conversations
Speaker 1: on okay, where does the value accrue in this chain?
Speaker 1: Where does most of the work get done? What do
Speaker 1: these products like expose themselves to in the life of
Speaker 1: the user. Is it's something deeply under the hood or
Speaker 1: is it integrated into their daily operations? Do they know
Speaker 1: they're using an AI do they know they're using software?
Speaker 1: Is it something that they're like directly typing in or
Speaker 1: is it something that they're interfacing with someone who's doing
Speaker 1: the typing on their path, etcetera, etcetera. Well, Patrick, this
Speaker 1: was absolutely great talking to you. We could talk for
Speaker 1: a long time, but instead we'll just talk to you
Speaker 1: in a few weeks again when we have a million
Speaker 1: more questions. Now, I'm big patigias, but I learned a
Speaker 1: lot and really appreciate you coming back on the show.
Speaker 1: Thanks very much for having me, and I always happy
Speaker 1: to be come back. Thanks Patrick. That was fun, so Tracy,
Speaker 1: there were so many interesting elements of that conversation. I'm
Speaker 1: really glad we had Patrick back. I'm not even sure
Speaker 1: where to begin, but to start, you know, his point
Speaker 1: about the hiring boom during the pandemic, I thought was interesting,
Speaker 1: not just that maybe these companies had a sort of
Speaker 1: unrealistic expectations about how long does growth boo would last,
Speaker 1: but that when you're hiring that fast and under sort
Speaker 1: of extremely unusual situations, like you have that drift where
Speaker 1: maybe you're like there's a little bit of a we're
Speaker 1: not that happy with the class. And then also that point,
Speaker 1: but you also can't just hire everyone who came in
Speaker 1: recently for reasons of like seasoning and like skill level growth. Well,
Speaker 1: to me, it kind of I guess hammers home the
Speaker 1: point that three years on from the start of the
Speaker 1: COVID pandemic, we are still experiencing these normal developments. And
Speaker 1: it kind of gets to the macro versus micro point
Speaker 1: about some of the recent payrolls figures. You know, all
Speaker 1: the tech layoffs that have been announced. Are they saying
Speaker 1: something about the wider economy or is this really a
Speaker 1: tech specific problem? And I think, I mean, I can
Speaker 1: kind of argue it either way. I think I come
Speaker 1: away from that conversation thinking, well, you know, one were
Speaker 1: really unusual periods in terms of hiring for the big
Speaker 1: tech companies, and to some extent it seems reasonable that
Speaker 1: that starts to get rolled back a little bit. But uh,
Speaker 1: you know, it's also I take his point, is he
Speaker 1: and I suspect it's probably true, which is that if
Speaker 1: a year ago you were thinking you wanted to go
Speaker 1: into engineering or coding or something like that, very little
Speaker 1: about what we've seen so far in three should make
Speaker 1: you change your mind. I thought that was really interesting
Speaker 1: too about like sort of the questions about AI and
Speaker 1: how so it's like, as he pointed out, like there
Speaker 1: are other you know, places working on very similar, if
Speaker 1: not equal technology. What sort of made things breakthrough recently
Speaker 1: was the consumerization of some of the chat interface or
Speaker 1: some of these AI images imaging things. So like how
Speaker 1: much of like to go to market for this stuff?
Speaker 1: Ultimately isn't sort of like familiar experiences that people already have.
Speaker 1: It reminds me a lot and I don't mean this
Speaker 1: necessarily in a negative way, but it reminds me a
Speaker 1: lot of crypto in the sense that, like, yes, there
Speaker 1: is a lot of hype around AI, but also in
Speaker 1: the sense that this is a new technology that people
Speaker 1: can actually participate in. And so the use of the
Speaker 1: AI image generators, chat GPT, it kind of brings it
Speaker 1: to people in the same way that they are able
Speaker 1: to experiment with, you know, blockchain and different types of
Speaker 1: money using crypto, and so it suddenly becomes a lot
Speaker 1: more salient for people in that way. Yeah, you know,
Speaker 1: like there's a good example because like with crypto, like
Speaker 1: if you're like interacting with like core protocols are like
Speaker 1: developing on ethereum like that's going to be a limited
Speaker 1: a limited number of people know how to do that.
Speaker 1: But if you're building like an exchange, there are a
Speaker 1: lot of I mean, everyone can have a wallet, right
Speaker 1: you're marketing or stuff like that. There are still all
Speaker 1: of these roles within crypto that have like sort of
Speaker 1: like consumer facing analogs to any other industry. Yeah, I
Speaker 1: need to look up the compile tiler. That sounds interesting. Yeah,
Speaker 1: I'm gonna go off in Google um deep learning compiler.
Speaker 1: I guess for so far a job security, we still
Speaker 1: need to learn to code. Huh. I think we need
Speaker 1: to learn AI. I don't know. Probably I don't know,
Speaker 1: but you know C plus plus, which is what I
Speaker 1: learned in a little bit of no because it's obsolete.
Speaker 1: Like no, I don't think anyone uses c post plus
Speaker 1: and they certainly don't use it for for AI and
Speaker 1: machine learning stuff. I should have asked Patrick what language?
Speaker 1: What coding language Python? When we have them back in
Speaker 1: the Yeah, okay, yeah, our next episode with Patrick will
Speaker 1: be about which coding language we should all be learning
Speaker 1: in the future. Shall we leave it there? Let's leave
Speaker 1: it there. This has been another episode of the All
Speaker 1: Thoughts podcast. I'm Tracy Alloway. You can follow me on
Speaker 1: Twitter at Tracy Alloway. And I'm Joe wisn't Thal. You
Speaker 1: can follow me on Twitter at the Stalwart. Follow our
Speaker 1: guest Patrick McKenzie. He's at Patio eleven and check out
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Speaker 1: for listening