Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 3: I'm Joe Wasnal and I'm Tracy Alloway. Tracy.
Speaker 2: We're recording this February eleventh, and IGV the software ETF
Speaker 2: down another three percent today.
Speaker 4: It has been ugly in software. Everyone's throwing around the
Speaker 4: term SaaS apocalypse. I mean, the great thing about SaaS
Speaker 4: is there are a lot of things that like rhyme
Speaker 4: with it, a lot of hominems, so you can you
Speaker 4: can make all those puns. Yeah, exactly, SaaS is trash whatever.
Speaker 4: But I'm looking at the share price of Salesforce, oh yeah,
Speaker 4: in particular because I always think of Salesforce as sort
Speaker 4: of like emblematic poster child. Yeah, post child of like
Speaker 4: a software company that I'm not really sure what they do,
Speaker 4: but yeah, it's it's just ugly.
Speaker 2: It's basically been cut in half, hasn't it. Since it's
Speaker 2: peaked like an early twenty twenty five. Right now it's
Speaker 2: one eighty four eighty.
Speaker 4: Four, and it's all your fault, shoe.
Speaker 3: It's all my fault.
Speaker 2: That's right, because earlier in the year, after we got
Speaker 2: back from Christmas, vacation or Christmas break, you know, around
Speaker 2: that I'd seen everyone playing around with claud code, and
Speaker 2: then I had to do it. We did an episode,
Speaker 2: and so people were like, oh, if Joe Wisittal can
Speaker 2: like figure out claud code, that there must not be
Speaker 2: any value to any of these companies at all.
Speaker 5: There.
Speaker 2: You know, you mentioned Salesforce. That's far from the ugliest one.
Speaker 2: I'm looking at Atlassian, which makes a lot of like
Speaker 2: workforce productivity companies, like some Slack competitors and stuff. That
Speaker 2: was a four hundred and fifty dollars stock back in
Speaker 2: twenty twenty one. That's an eighty six dollars stock. So like, yeah,
Speaker 2: it's ugly, and yeah, as you said, everyone is realizing
Speaker 2: that if any old fool can write software, maybe these companies, yeah,
Speaker 2: I don't have much value.
Speaker 4: I mean, I will just say it's not just software
Speaker 4: right now. So we're seeing sort of rolling series of
Speaker 4: concerns where like every time AI does something or create
Speaker 4: some new product, it hits a particular industry. So on
Speaker 4: Monday it was the insurance industry, insurance brokers, and you
Speaker 4: know today Wednesday, February eleventh, I think it's some of
Speaker 4: the stockbroker firms.
Speaker 2: And yeah, all you have to do is just say
Speaker 2: AI industry and there's a you know, it's really there's
Speaker 2: a lot of anxiety. But there's something that doesn't make
Speaker 2: any sense to me about this or the thing that
Speaker 2: I'm wrapping my head around.
Speaker 3: It's like, sure, any of.
Speaker 2: Us could easily like write some software, but like writing
Speaker 2: software is a cost center for these companies.
Speaker 1: Right.
Speaker 2: If you're salesforce and you can trivially reduce the cost
Speaker 2: of building software, that's also a benefit for you. And
Speaker 2: there's a lot more to a software company than just
Speaker 2: code generation, because there's all kinds of you know, network
Speaker 2: effects and links into this. It's like a software company
Speaker 2: is clearly more than just code, and so the fact
Speaker 2: that maybe code can be generated a lot cheaper does
Speaker 2: not screen to me, like, oh, these companies are worth
Speaker 2: lessing views.
Speaker 4: Sure, but at the same time they've been pricing. Their
Speaker 4: pricing is based on that assumption, right, like that there
Speaker 4: is no competitor for what they're doing, and suddenly you
Speaker 4: might have an in house competitor.
Speaker 2: Absolutely, But you know, it's like network effects and do
Speaker 2: companies want to start like building their own like payroll
Speaker 2: software Anyway, I have a lot of questions about this
Speaker 2: sell off, and to your point.
Speaker 4: No, no, no, this is you doing like penance first,
Speaker 4: for causing causing the sell off.
Speaker 2: All right, let's talk to someone who actually might be
Speaker 2: able to answer some of these questions for us. We're
Speaker 2: gonna be speaking to someone who's been in the software space,
Speaker 2: an investor in the software space for a long time,
Speaker 2: recently put out a great deck really diving into SaaS
Speaker 2: of the SaaS apocalypse and what kinds of companies are
Speaker 2: thriving and what kinds of companies were struggling even before
Speaker 2: everyone started talking about AI code generation and all that.
Speaker 2: We're gonna be speaking with Jared Sleeper. He is a
Speaker 2: partner at Avenue, which does a growth investing private company.
Speaker 3: So, Jared, thanks for coming on off.
Speaker 5: Yeah, my pleasure. Excited to be here.
Speaker 2: Why are we talking to you? Just you know, for
Speaker 2: our listeners. Apparently your first time on a podcast, which
Speaker 2: is crazy. But why are we talking you? Give us
Speaker 2: a little bit about.
Speaker 3: Your background investing in software and understanding the space.
Speaker 6: Yeah, my pleasure. So I think one thing that makes
Speaker 6: me a little bit different in the investor world is
Speaker 6: that I've spent time investing in early stage startups, public companies,
Speaker 6: and everything in between. So I spent a chunk of
Speaker 6: my career at an early stage venture fund in Boston
Speaker 6: called Matrix Partners, working with an og SaaS investor named
Speaker 6: David Scock, and then was also at ko TU where
Speaker 6: I ran public software. And so I've kind of have
Speaker 6: this like experience across a spectrum from ground floor startups
Speaker 6: to looking at the big public companies, which I've done
Speaker 6: for the last ten years.
Speaker 3: Perfect guest, Perfect guests.
Speaker 4: So give us some color on the mood in software
Speaker 4: at the moment. Are people like I don't know, hunkering
Speaker 4: down in their bunkers. How bad is it?
Speaker 5: Yeah?
Speaker 6: I get texted constantly from folks on the byside, just
Speaker 6: you know, retrenching. I can't believe this is happening, can't
Speaker 6: go lower. I keep saying that it's one hundredth time
Speaker 6: I bought the DIP. You use the SaaS apocalypse like
Speaker 6: casastrophe is my science. It's definitely one of those moments.
Speaker 6: And we were talking about this a little bit earlier
Speaker 6: before starting, But one of the things about software that's
Speaker 6: really fascinating is there's very few folks, even on the
Speaker 6: buy side who really understand how software works. It's one
Speaker 6: of those roar shot tests kind of sectors where no
Speaker 6: almost no one's logged into Salesforce and clicked around much
Speaker 6: less than a Salesforce admin and understood the full complexity.
Speaker 6: And so when there's panic, there's not a lot of
Speaker 6: support for the stocks, and people, you know, get scared
Speaker 6: very easily.
Speaker 3: Well, explain what this means.
Speaker 2: So, for example, in a lot of companies, it's like
Speaker 2: you're saying that the people who investor trade these stocks,
Speaker 2: they just know them as financial tables basically, and they
Speaker 2: have some idea of their financials and some idea of
Speaker 2: their customer base, et cetera. But they don't have like
Speaker 2: a great intuition for the product onlike say, you know
Speaker 2: people who use Instagram and therefore might have a feel
Speaker 2: about meta for example.
Speaker 6: Yeah, if you're an investor in Lulu Lemon, you have
Speaker 6: a pretty solid conception of what that business is. You
Speaker 6: can go into the scioga pants exactly. You can buy
Speaker 6: the product to ship it to yourself. If you're an
Speaker 6: investor in Viva, which makes CRM software for pharmaceutical reps,
Speaker 6: I bet you there's almost no investors in Viva who
Speaker 6: have ever been inside the product even once, much less
Speaker 6: used it on a day to day basis and understood
Speaker 6: how it works.
Speaker 4: So I'm going to go way back in time and start,
Speaker 4: I guess the very beginning, But why is it that
Speaker 4: software like this, you know, payment management systems, whatever, Why
Speaker 4: were they historically not developed in house?
Speaker 3: Like?
Speaker 4: How did we get this model where we have these
Speaker 4: huge software companies that are really you know, to date
Speaker 4: have been really integral to a lot of businesses.
Speaker 6: Yeah, it's a great question. You know, back in the
Speaker 6: very early days of software, like back in the seventies
Speaker 6: or eighties, there was a lot done in house, and
Speaker 6: we've seen a very clear mixshift over time towards using
Speaker 6: third party software. And what it comes down to is
Speaker 6: the software was expensive to build and maintain, and there's
Speaker 6: this need for an ecosystem of integrations around it, which
Speaker 6: are also expensive to build and maintain. And so if
Speaker 6: you look at a software company, it can afford to
Speaker 6: have one, two, three thousand engineers plus partnership teams, et cetera,
Speaker 6: all working to build the perfect piece of software for
Speaker 6: a given application, and then what's striking And this will
Speaker 6: come up a lot more in this conversation, is not
Speaker 6: selling it for that much money.
Speaker 5: Right.
Speaker 6: A lot of software companies report a stat which is
Speaker 6: the share of our customers that pay us more than
Speaker 6: one hundred thousand dollars a year, And one hundred thousand
Speaker 6: dollars a year is less than half of the fully
Speaker 6: loaded cost of a software engineer, right. And so the
Speaker 6: software model was build a product that can be applied
Speaker 6: to thousands of customers and it's the same product for
Speaker 6: every customer, and then sell it to them for way
Speaker 6: cheaper than they could ever hope to build it themselves,
Speaker 6: even less in the cost of one employee.
Speaker 2: Okay, I'd love to just talk long term software history
Speaker 2: even before you know, we think a lot about SaaS
Speaker 2: and these startups and stuff like that. But like a
Speaker 2: lot of the big companies that we think of in software,
Speaker 2: especially like pre Salesforce, whether it's like SAP Oracle Microsoft obviously,
Speaker 2: aren't there a bunch of third party companies whose job
Speaker 2: is to just like help install it for you, Ye,
Speaker 2: like an SAP installed, And that'll be a totally separate company.
Speaker 2: Because it's so big and it's so unwieldy and complex
Speaker 2: that you actually you can't just like install it yourself,
Speaker 2: or it has to be customized or whatever.
Speaker 6: And there's two parts to that which I think are important.
Speaker 6: One is the integrations into your existing systems. Right, a
Speaker 6: lot of big old companies have old databases, old applications,
Speaker 6: and it's important for everything to be stitched together. So
Speaker 6: you need software engineers and you know, consultants to go
Speaker 6: in and understand those existing systems and kind of get
Speaker 6: them linked up to the new systems. But the other one,
Speaker 6: which is probably bigger, is just people management and change management.
Speaker 6: You know, any software system is the combination of the
Speaker 6: code and all of the individual users who have learned
Speaker 6: how to use it. If you're trying to change out
Speaker 6: your CRM at a company, that means training every single
Speaker 6: sales rep on how to use the new CRM and
Speaker 6: getting it right. And if they get it wrong, then
Speaker 6: you lose deals that quarter. And so, you know, one
Speaker 6: of the kind of tropes in investing is if you
Speaker 6: see a company that's doing an ERP transition. ERP stands
Speaker 6: for Enterprise Resource planning. It's the kind of core software
Speaker 6: accounting you know, supply chain, et cetera. That company's probably
Speaker 6: going to miss its earnings over the next one or
Speaker 6: two quarters because those transitions are so painful and so yes,
Speaker 6: there's a big consulting complex around it that does its
Speaker 6: best to come in and parachute in the talent that's
Speaker 6: required to make those transitions smooth. And that tells you
Speaker 6: something about what makes software so sticky, or at least
Speaker 6: has historically.
Speaker 4: It's third party agents all the way down, I feel.
Speaker 4: But actually, on this note, so we hear the integration
Speaker 4: point brought up a lot, and I think the very
Speaker 4: first episode we did on claud code we talked a
Speaker 4: little bit about it as well. But like, if you
Speaker 4: have something like claud code where you can just give
Speaker 4: it permissions to make changes to your computer, does some
Speaker 4: of that integration expertise actually start to go away, because
Speaker 4: presumably we are going to get AI. I would assume
Speaker 4: at some point, given the rate that it's developed and improving,
Speaker 4: that we'll be able to do this like plug itself
Speaker 4: into various systems.
Speaker 6: Yeah, one hundred percent. I think the challenge of writing
Speaker 6: the code for the integrations is going away. That's not
Speaker 6: the bulk of the challenge for a majority of integrations.
Speaker 6: It's about really deeply understanding the prior system and how
Speaker 6: it maps to the new system. And the reality is,
Speaker 6: within most organizations that's a human problem. It's hey, this
Speaker 6: column says status two thousand and four, what does that mean? Like?
Speaker 6: How does that map to the new system that we're building?
Speaker 6: So you have to go talk to someone and understand it.
Speaker 6: And so there's certain types of integrations where I think
Speaker 6: they're effectively solved problems now because you can write a
Speaker 6: quick write into chat in to clog code and get
Speaker 6: a perfectly written piece of software to make it happen.
Speaker 6: And then there's others that are just fundamentally human problems
Speaker 6: because the data doesn't exist in digital space.
Speaker 2: Let's talk more about that, because really it is pretty
Speaker 2: extraordinary the degree to which I don't know it's the
Speaker 2: working code. I don't know if it's high quality code,
Speaker 2: but certainly these models can generate working code, and it's
Speaker 2: just it blows my mind whenever I use it. But
Speaker 2: talk to us a little bit more about from the
Speaker 2: perspective of various software vendors, and I'm sure there's a
Speaker 2: range about what they're selling and how much is it
Speaker 2: code versus how much is it other stuff, and which
Speaker 2: ones are more exposed to the pure like code generation ability.
Speaker 6: Yeah, it's a great question, and you're one hundred percent right.
Speaker 6: It's producing working code, and frankly it has been for
Speaker 6: the last year or so. I built my first leveable
Speaker 6: app that was working in production about about a year ago,
Speaker 6: and it's even intensified in the last three months.
Speaker 1: Right.
Speaker 6: I think when people buy software, there's a set of
Speaker 6: things that they're buying. One thing that I think is
Speaker 6: important for everyone understand is that open source software has
Speaker 6: been a thing, and there have been free, open source
Speaker 6: versions of almost any software you could buy for all
Speaker 6: of recorded history. There's actually some companies that are public
Speaker 6: that built their businesses packaging that open source software and
Speaker 6: adding a few custom features and then support on top
Speaker 6: of it. Because of the companies reliant on an open
Speaker 6: source database or a company like Elastic with its Elastic
Speaker 6: Search product, which is an infrastructure tool and it breaks,
Speaker 6: they need someone to call, both for COIA reasons and
Speaker 6: because it can be very complex and technical and they
Speaker 6: need to quickly understand it. And so that has been
Speaker 6: a big part of the story historically, is that need
Speaker 6: to you know, have support. Another thing that you sell
Speaker 6: when you sell as a software vendor is what I
Speaker 6: call herd familiarity, which means everyone on Earth knows how
Speaker 6: to use your software, which just simplifies the training and
Speaker 6: onboarding workflow. I'll give a few examples because I'm sure
Speaker 6: it's a new term for listeners since I.
Speaker 5: Made it up.
Speaker 6: You know, Zoom is a great business. Microsoft has been
Speaker 6: giving away a free version of the product forever in teams.
Speaker 6: Why do people use Zoom Because in certain industries almost
Speaker 6: everyone knows how to use Zoom. They have their Zoom setup,
Speaker 6: they have their virtual background chosen. They're not going to
Speaker 6: fumble around for the first minute or two on the call,
Speaker 6: and that's well worth the twenty dollars a month to
Speaker 6: have a Zoom plan. But that applies to lots of
Speaker 6: other areas as well. So think about Microsoft Excel for example.
Speaker 6: You might be able to use Google Sheets to do
Speaker 6: the same thing, but you really want to retrain every
Speaker 6: person who comes in on the Google Sheets shortcuts versus
Speaker 6: Excel shortcuts. It's not a good use of time, especially
Speaker 6: when the software is already so cheap. And so that's
Speaker 6: another plank in what people are buying when they buy
Speaker 6: software is the standardization and the knowledge that they'll be
Speaker 6: able to hire employees who have that, and then there's
Speaker 6: things like brand again, the kind of ecosystem that comes
Speaker 6: around it, and so it really is more than just
Speaker 6: the raw code.
Speaker 4: We've been joking about this, but the idea of software
Speaker 4: companies value lying in being a scapegoat essentially for when
Speaker 4: things go wrong is kind of funny and dystopian, I
Speaker 4: think in many ways.
Speaker 6: Yeah, I mean, I think you know, it's a real fear,
Speaker 6: right And the way I think about it is, there
Speaker 6: are two arguments against software right now. One is the
Speaker 6: world is going to stay the same, but software just
Speaker 6: going to get a lot cheaper over time now that
Speaker 6: it's cheaper to build. And I think there's no one
Speaker 6: who would argue that it's not gotten dramatically cheaper to
Speaker 6: build for reasons that we laid out in our deck
Speaker 6: and we can talk through it more. We don't buy
Speaker 6: that argument. I don't buy that argument. But the second
Speaker 6: is the world's about to get really weird and the
Speaker 6: way that knowledge work happens is going to change. And
Speaker 6: if we think out three, four or five years, who
Speaker 6: knows if there will even be customer support reps or
Speaker 6: sales reps or software engineers, and I think that's what's
Speaker 6: causing the kind of hit to the share prices lately,
Speaker 6: is this terminal value concern.
Speaker 3: Yeah, it was interesting.
Speaker 2: So one of the companies that's been associated with the uh,
Speaker 2: what did you say casastrophe?
Speaker 3: One of those companies that's been.
Speaker 2: Caught up with this blue owl, the private investing firm
Speaker 2: Private Credit. I read through their conference call and their
Speaker 2: CEO was like, not only do we not see red lights,
Speaker 2: not only do we not even see yellow lights, we
Speaker 2: actually see a lot of green lights, which I think
Speaker 2: is really interesting.
Speaker 3: Because it can fit with this idea of.
Speaker 2: This year could be fine, next year could be fine,
Speaker 2: year after that could be fine, and then the year
Speaker 2: after that could be zero or at least that's the
Speaker 2: anxiety that there's this terminal value talking.
Speaker 4: That's like a cliff rest.
Speaker 5: Yeah, there's this cliff Yeah. I think it's really helpful.
Speaker 6: You know, this is our second iteration of the deck,
Speaker 6: and so we kind of force ourselves to recenter on
Speaker 6: what actually happened since the last deck, right, and there's
Speaker 6: a very clear pattern in software and what happened over
Speaker 6: the last five years, which is the pandemic. People freaked
Speaker 6: out at the beginning, but it was rapidly clear that
Speaker 6: it was an accelerant for SaaS as everyone tried to
Speaker 6: digitize their companies, and so you had a spike in
Speaker 6: the growth rate and net retention of the businesses. It
Speaker 6: peaked at just over forty percent and twenty twenty one
Speaker 6: for the median software companies. That's really nice annualized growth.
Speaker 6: And then there was a hangover and that slowed down,
Speaker 6: and we wrote eighteen months ago that that reflected the
Speaker 6: sector sort of maturing. The adoption had just slowed down
Speaker 6: because most folks had adopted the software that they needed
Speaker 6: under the pressure of the pandemic. And so for the
Speaker 6: last few years after that, we saw this degradation and
Speaker 6: growth rates across the sector. By the beginning of last year,
Speaker 6: the median company was growing eighteen percent instead of forty percent,
Speaker 6: so you saw a pretty significant draw down. What's fascinating
Speaker 6: is that if you look at the actual financial performance
Speaker 6: of the companies in the last year, it's been pretty good.
Speaker 6: That growth rate has held. It was eighteen percent again
Speaker 6: in Q three. Net retention has also been consistent at
Speaker 6: about one hundred and ten percent. That's revenue from existing
Speaker 6: customers over the same revenue from those customers a prior year.
Speaker 6: So there's not a churn issue developing or a lack
Speaker 6: of expansion within the customer base, and a lot of
Speaker 6: the companies are actually accelerating growth or guiding to accelerating growth.
Speaker 6: We have a chart showing the number of those companies
Speaker 6: has increased each quarter of the last three successive quarters.
Speaker 6: And so there's a lot going on right now with
Speaker 6: the terminal value. But it's very hard to argue that
Speaker 6: this is something that's happening today and showing up in
Speaker 6: the numbers. The thing is investors are sharp, right, and
Speaker 6: they're constantly looking at that booking.
Speaker 5: Yeah.
Speaker 6: I mean look at CHEG right, which went down very
Speaker 6: quickly in the aftermath of chat GPT coming out, and
Speaker 6: that was completely correct.
Speaker 5: Right.
Speaker 6: Investors were ahead of that, and of course for the
Speaker 6: first few quarters the management team of CHEG, you know,
Speaker 6: had their heads in the sand, but then it became
Speaker 6: clear that it really was existential to their business.
Speaker 4: That's a fun chart.
Speaker 2: I thought I was looking at a typo because I saw, wow,
Speaker 2: that was a near one hundred dollars stock in February
Speaker 2: twenty twenty one.
Speaker 3: It is now a sixty one cent.
Speaker 6: Stock and you have to give the markets credit. Like
Speaker 6: the second chat of T came out, people were like,
Speaker 6: this company's in big trouble. They didn't wait for it
Speaker 6: to hit the financial results, and so there is signal
Speaker 6: and what people think.
Speaker 4: I have a bunch more questions, but just briefly, where
Speaker 4: does data actually fit into all of this? Because the
Speaker 4: other thing we hear about AI is maybe the models
Speaker 4: don't matter that much, but it's the actual data that
Speaker 4: you have access to. And I imagine the customers themselves
Speaker 4: of SaaS companies, they have their own data. Do the
Speaker 4: SaaS companies have their own data as well? Can they
Speaker 4: build off of that?
Speaker 6: Yeah, it's a great question. And we're here at one
Speaker 6: of the world's biggest data companies, so very apt. Well,
Speaker 6: disclosure data is definitely something that gets more valuable in
Speaker 6: this world. If you think about a stylized AI model,
Speaker 6: it could have PhD level intelligence in a domain. But
Speaker 6: if you hired a PhD in your company and sat
Speaker 6: her down on her first day, she wouldn't be very useful, right.
Speaker 6: She would have to understand how the organization functions, where
Speaker 6: things live. Do I trust this chart or that chart.
Speaker 6: I need access to the Google drive, I need access
Speaker 6: to Slack, I need to spend some time reading up
Speaker 6: and so we call this kind of context.
Speaker 5: Right.
Speaker 6: It's all the extra information that an AI needs to
Speaker 6: get something done, no matter how intelligent it is. And
Speaker 6: we wrote about this in the chart in the deck.
Speaker 6: But there's a real question of who becomes that system
Speaker 6: of context. And you're right, a lot of the software
Speaker 6: companies do sit on a pool of very important data.
Speaker 6: Let's talk about Salesforce for example.
Speaker 5: Right. CRM is where.
Speaker 6: You track the records of every customer. You have every
Speaker 6: prospect in your pipeline, all of your historic interactions with them,
Speaker 6: notes from sales reps on what's going on the status
Speaker 6: of their account, their customer support requests. It's an incredibly
Speaker 6: complex piece of software for a large enterprise, and obviously
Speaker 6: if you are an AI agent working within a company,
Speaker 6: you would need access to that in order to get
Speaker 6: almost anything done. Right, But you need more than is there.
Speaker 6: You don't know what happened at the sales dinner last
Speaker 6: night unless the rep took really detailed notes. And I
Speaker 6: can tell you one comment learning and software is they
Speaker 6: do not take very detailed notes.
Speaker 5: Especially have a sales party, right, yeah, exactly.
Speaker 6: Can people assume that software management teams know exactly what's
Speaker 6: going on, but they're looking through really messy salesforce data
Speaker 6: and doing their very best.
Speaker 4: Now I'm imagining a sales agent being like the Cabernet
Speaker 4: was exquisite at last night's party, just putting in all
Speaker 4: these irrelevant like diary entries, exactly.
Speaker 6: But a lot of that context does live in human brains.
Speaker 6: You know, a sales rep meets a person at dinner,
Speaker 6: gets to know their kids, figures out what sports team
Speaker 6: they root for, and they're not automatically pumping all that
Speaker 6: into the CRM. And so there's this race to collect
Speaker 6: the information that an AI agent would need in order
Speaker 6: to actually take proactive action, and the software companies have
Speaker 6: a position there. But there's also this set of AI
Speaker 6: native startups that are coming in building actual agents who
Speaker 6: are doing their own work to collect that context.
Speaker 5: And that's one of the.
Speaker 6: Battles that we saw, you know, kind of highlighted in
Speaker 6: our deck is whoever wins that has a chance to
Speaker 6: be a really valuable company.
Speaker 2: You know what I think about and I think you
Speaker 2: talk about this in your deck, But when I think
Speaker 2: about software, I sort of have like if there's a spectrum,
Speaker 2: you know, I think about Salesforce dot com, which is
Speaker 2: a platform, and there's third party developers that build on
Speaker 2: top of Salesforce and they sort of offer any everything,
Speaker 2: And then I think about something niche like this is
Speaker 2: the company that makes point of sales software for dentist's office,
Speaker 2: and they went around by giving them free payment terminals,
Speaker 2: and they joined y Combinator, and you know, they signed
Speaker 2: up ten thousand dentist office and then they pay those
Speaker 2: offices pay them ten dollars a month forever for access
Speaker 2: to that. You know what, I'm just making it up,
Speaker 2: but things like that. Is there a side of the
Speaker 2: spectrum that's more at risk here? Is that spectrum legitimate
Speaker 2: way to think about the industry or is there threats
Speaker 2: on sort of wherever you look?
Speaker 6: Yeah, it's a great question. I mean, certainly in the
Speaker 6: world gets really weird scenario, it's not clear there's anywhere
Speaker 6: immune from threats, but it's important to think through what
Speaker 6: it looks like. I think what's most at threat is
Speaker 6: companies that serve enterprises with very customized software already or
Speaker 6: software that takes a very heavy implementation. And the reason
Speaker 6: is if anyone's going to take advantage of this wave
Speaker 6: of technology to really you know, advance and replace a
Speaker 6: core system of software, it's going to be the enterprises
Speaker 6: that have the resources and the customization needs. If you
Speaker 6: think about SMBs. You know, my dad runs our family's
Speaker 6: grocery store in the family for one hundred years, and
Speaker 6: he just changed his point of sale for the first
Speaker 6: time in a few decades, and it was a really
Speaker 6: messy process.
Speaker 5: It took a long time. Your dad, come on all alls, Yeah, sure,
Speaker 5: we love, we love to.
Speaker 6: All about independent grocery. And but you know he's certainly
Speaker 6: not going to sit down and vibe code himself a
Speaker 6: point of sale system and put the store on it.
Speaker 6: I can guarantee you that, nor will any dentist.
Speaker 5: Right.
Speaker 6: There's a chance that someone comes along with a cheaper version,
Speaker 6: but you know, I think that's not something he's going
Speaker 6: to switch to anytime soon. He's going to go through
Speaker 6: that pain for another few decades to come right. And
Speaker 6: so it really is, you know, kind of company by company,
Speaker 6: Like I'm doing this exercise right now on X where
Speaker 6: every day I look at a different software company and
Speaker 6: just think hard about what will AI look like for
Speaker 6: this company, And it's really interesting when you press I'll
Speaker 6: give an example like DocuSign, which I think to most
Speaker 6: investors would seem like an incredibly simple, easy piece of software. Right,
Speaker 6: it's an eating software. We've all experienced it. DocuSign has
Speaker 6: more employees today than open ai and andthrop it combined god,
Speaker 6: which is a crazy stat and probably reflects that labor
Speaker 6: is inefficiently allocated across the market. But when you actually
Speaker 6: double click into what DocuSign does, there are ways in
Speaker 6: which it's very complicated. Right understanding the signature regulations in
Speaker 6: every country around the world, what does it take for
Speaker 6: signature to be legally valid. Most of its signatures are
Speaker 6: done as an API, so folks are integrating it into
Speaker 6: their own applications. And there's a benefit to using docu
Speaker 6: sign which is the brand people have been giving away
Speaker 6: free e signature software for a very long time. But
Speaker 6: if you're a company of a certain esteem, you want
Speaker 6: to make sure your customers trust what they're signing, and
Speaker 6: if they're getting a contract from you, you'd much rather
Speaker 6: say DocuSign than xyz sign that someone vibe coded, right,
Speaker 6: And so I think it's really important to look a
Speaker 6: company by company. It's definitely a stock pickers market where
Speaker 6: there's some that are either relatively immune or have a
Speaker 6: chance to benefit, and tho's others that could be in
Speaker 6: real trouble.
Speaker 4: So is the argument the bold case for software, or
Speaker 4: at least the non sudden death case for software. This
Speaker 4: idea that like, Okay, if you have a software company
Speaker 4: that's producing I don't know, like DocuSign, you're able to
Speaker 4: sign documents digitally and track them and share them and
Speaker 4: all of that. You can build more quickly and more
Speaker 4: efficiently off of that base model and provide new versions,
Speaker 4: new customizations for customers. So I could do DocuSign for dentists,
Speaker 4: just to stick with that example. I don't know what
Speaker 4: specific needs dentists would have. I don't know, maybe marking
Speaker 4: up like teeth or something. Yeah, and then I can
Speaker 4: do like DocuSign for doctors and DocuSign for sales agents
Speaker 4: or whatever and just keep going.
Speaker 5: Yeah.
Speaker 6: I think that's right. I actually kind of think of
Speaker 6: it as there's three cases. There's the software gets wiped
Speaker 6: out case, there's the not much happens to software case,
Speaker 6: and then there's the bual case where the software companies
Speaker 6: capture a lot of value. I think it's a little
Speaker 6: different than them adding a lot of features and functionality. Frankly,
Speaker 6: I think a lot of software products today are pretty mature.
Speaker 6: There than thousand engineers working on them for ten years,
Speaker 6: and they've built not all, but most of the things
Speaker 6: that you'd want to build with today's technology. But with agents,
Speaker 6: there's ways to automate a big chunk of the work.
Speaker 6: So software company that's done this very well is Intercom.
Speaker 6: Intercom sells customer support software. It's those little widgets on
Speaker 6: the bottom right hand corner of websites. They were the
Speaker 6: creators of that. They had a nice business, but then
Speaker 6: they got very aggressive about building out an AI product
Speaker 6: called Finn, which answers customer support queries on its own.
Speaker 6: And they've i think they've mentioned that it's almost one
Speaker 6: hundred million of ARR now on a base that was
Speaker 6: you know, like three hundred million of ARR or something
Speaker 6: like that. And so they've really reaccelerated their business by
Speaker 6: building an AI native tool that actually solves the work,
Speaker 6: not just a tool that not just kind of exists
Speaker 6: as a tool that humans use. And so yeah, I
Speaker 6: think that's like the mega bowlcase, right. I think about
Speaker 6: it like almost a transition from brick and mortar retail
Speaker 6: to e commerce, where you have a brand new way
Speaker 6: of doing business and you have a bunch of legacy
Speaker 6: companies and some of them will probably just exist as
Speaker 6: they always have. Others can benefit from the change and
Speaker 6: add new business lines. You look at Walmart's share price,
Speaker 6: it's done amazingly well at incorporateing e commerce into its business.
Speaker 6: And then there's going to be some that are like
Speaker 6: Seers and go away.
Speaker 4: That's funny. Sears always reminds me of my dad loves
Speaker 4: Sears because he always said the parking lot was empty
Speaker 4: when he goes to the shopping mall, so he always
Speaker 4: went through Sears anyway. So I understand like the cost
Speaker 4: argument and brings down the cost of code. Maybe you
Speaker 4: have fewer employees or whatever, But where does growth actually
Speaker 4: come from in that world? How are you expanding your
Speaker 4: customer base?
Speaker 6: Yeah, you're really going to them and saying we are
Speaker 6: replacing human labor. And there's a different pricing paradigm.
Speaker 7: Now.
Speaker 6: You used to think of us as something you paid,
Speaker 6: you know, twenty thirty forty fifty dollars per seat, per
Speaker 6: month for as a tool for your employees, almost as
Speaker 6: if you know your employees are artisans and they're getting
Speaker 6: a toolkit to work with. And now we're just selling
Speaker 6: you an employee or the results of an employee. So
Speaker 6: you know, we will sell you customer support tickets getting
Speaker 6: closed out for fifty cents or a dollar per ticket,
Speaker 6: and you can do math of what it would cost
Speaker 6: you for the human to do that, or what it
Speaker 6: would cost you for AI to do that, and we'll
Speaker 6: be cheaper. But we're also dramatically increasing what you pay
Speaker 6: us because you know, we're cutting into a completely different stream.
Speaker 6: And so that's what I think it looks like. We
Speaker 6: see a lot of exciting examples in the startup space
Speaker 6: of companies that are getting much much higher pricing.
Speaker 2: This is a totally new pricing model for software. If
Speaker 2: you just recorded another episode and they guess teased that,
Speaker 2: but talk to us about like results based pricing, talk
Speaker 2: to us.
Speaker 6: Yeah, it's results based pricing. There's a lot of questions
Speaker 6: on how it'll ultimately shake out. Fundamentally, what these companies
Speaker 6: are doing is they are reselling intelligence.
Speaker 5: Right the core model.
Speaker 6: Vendors Open Eye and Thropict, Google have created a way
Speaker 6: to get elastic intelligence. And if you have the right
Speaker 6: data and you can put the right harness around it,
Speaker 6: you can now sell that to your customers. What's an
Speaker 6: open question is how do you price that relative to
Speaker 6: the intelligence. So I was talking to someone this morning.
Speaker 6: You said, I think fifty percent growth smart on intelligence
Speaker 6: are about right. But we see a lot of variants
Speaker 6: in how startups are doing it today. Some are getting
Speaker 6: eighty percent gross margins on top of the model vendors.
Speaker 6: Others are getting twenty percent. But what's absolutely true in
Speaker 6: any case is if you're able to do that, you
Speaker 6: get much much higher pricing in total dollars than you
Speaker 6: did before orders a magnitude in some cases.
Speaker 4: But just to be clear, like the cost savings, it
Speaker 4: can't be priced so high that the company that's using
Speaker 4: the software to produce these outcomes like isn't saving money, right,
Speaker 4: that's the balancing act.
Speaker 6: One hundred percent. But I think if you think about
Speaker 6: when we talk about this a little bit earlier, think
Speaker 6: about where software pricing was already right, you know, think
Speaker 6: about salesforce. You know, at the elite tier, you know,
Speaker 6: eighty ninety one hundred dollars per user per month. So
Speaker 6: for round numbers, say one thousand dollars per user per year,
Speaker 6: for sales reps who could be making on average two
Speaker 6: hundred and fifty three hundred thousand dollars per year, if
Speaker 6: you have a technology that can come in and replace
Speaker 6: a sales rep, you can charge fifty thousand dollars still
Speaker 6: give the customer or a five x ROI, and then
Speaker 6: you have effectively fifty x your take rate on that revenue.
Speaker 6: And so that's the exciting opportunity. That's that's what has
Speaker 6: people excited in startup land for sure. If you talk
Speaker 6: to folks from Silicon Valley, they are foaming at the
Speaker 6: mouth about the opportunity to really expand tech spend in
Speaker 6: this way. And that's also the opportunity for the software
Speaker 6: companies that get it right.
Speaker 2: There must be another risk too, which is that if
Speaker 2: you can sort of resell intelligence it'd say an eighty
Speaker 2: percent gross margin, then for the model makers themselves, they're like, well,
Speaker 2: why do we just want to be this is gonna
Speaker 2: sound weird. Why do we want to be the dumb intelligence?
Speaker 5: Right?
Speaker 2: And that's sort of like we don't they use it.
Speaker 2: We don't want to be the dumb pipe. If we
Speaker 2: saw that like in the cloud era, right, the Asias
Speaker 2: and the Google Cloud and the Amazon One, they didn't
Speaker 2: want to just be commodity cloud and they started building
Speaker 2: like medical features. They wanted to differentiate themselves. So it
Speaker 2: must be a risk for the company's reselling intelligence that
Speaker 2: it's so lucrative. And then like, how are you thinking
Speaker 2: about the core model makers themselves? Yeah, and how they're
Speaker 2: thinking about expanding into some of these fields rather than
Speaker 2: just piping in intelligence for them.
Speaker 6: Well, look like in any situation, they're gonna have to
Speaker 6: make decisions, right, So in Amazon, you know, built aws,
Speaker 6: they had to decide where are we going to press
Speaker 6: and where are we not? Are we going to sell
Speaker 6: database software or are we going to let other vendors
Speaker 6: do that on top of us? And they kind of
Speaker 6: made those decisions as it went out. What's really interesting
Speaker 6: is if you look at the foundation model vendors, they
Speaker 6: have been racing towards the application layer. Both cloud code
Speaker 6: and cowork and open ai codex are applications that people
Speaker 6: download and use right, And I think that reflects this
Speaker 6: understanding that there is value in getting the users used
Speaker 6: to using your application. Otherwise, as you know, you may
Speaker 6: you risk being an API that's commoditized. People switch back
Speaker 6: and forth between you and that kind of application vendor
Speaker 6: has that control.
Speaker 4: So one of the advantages that software has is like
Speaker 4: this network effect comfort software as a security blanket for management. Right,
Speaker 4: But at the same time, people are getting really comfortable
Speaker 4: with AI, like telling them everything. And I keep thinking
Speaker 4: like if part of the sales pitch for software is
Speaker 4: like this sense of comfort, but then AI is rapidly
Speaker 4: becoming the thing that you talk to for everything. Does
Speaker 4: it eventually just become a portal for doing all these
Speaker 4: different things.
Speaker 6: It's a really interesting question, and this is where there's
Speaker 6: probably the biggest disparity between how enterprise buyers think and
Speaker 6: how humans think. Right, I'm sure you guys have seen
Speaker 6: claude Bot and the kind of rise of these, you know,
Speaker 6: open source agents that people are deploying for themselves, giving
Speaker 6: it and access to everything their whole computer, et cetera.
Speaker 1: That's Joe.
Speaker 3: Yeah, no, I didn't install I didn't install Club.
Speaker 4: Oh you didn't know.
Speaker 5: I'm getting mindset up. I lacked many with a hammer
Speaker 5: next to it.
Speaker 4: Well, I'm really curious why not?
Speaker 6: Because of this issue?
Speaker 2: Yeah, because of that and it just seemed like a
Speaker 2: potential waste of tokens and stuff.
Speaker 6: And yeah, and then then it turned out that for
Speaker 6: a while on Moltbook, which was the social media for
Speaker 6: all of the all the APIs are available in a
Speaker 6: public facing database and one could go read and so
Speaker 6: it was like a completely open system.
Speaker 5: That had to get fixed.
Speaker 6: And so, you know, enterprises really do worry about this stuff,
Speaker 6: and they worry about it for a good reason. You
Speaker 6: have another really interesting example. So there's a bunch of
Speaker 6: startups that help you record zoom calls and transcribe them.
Speaker 6: All of those zoom calls then become legally discoverable because
Speaker 6: they're transcribed somewhere, and so you have vcs in Silicon
Speaker 6: Valley who will refuse to use them, and you have
Speaker 6: other firms that are all in and recording everything that
Speaker 6: happens across the board so that they can upload that
Speaker 6: into AI as context. I think it's a really it's
Speaker 6: a really great point, you know. And one of the
Speaker 6: things that makes me wonder is companies that are willing
Speaker 6: to skirt the rules or you know, play fast and loose,
Speaker 6: will be moving much faster over the next two or
Speaker 6: three years. And One of the reasons big incumbents struggle
Speaker 6: is because they actually do have to care about this stuff.
Speaker 5: They have stuff to protect, they.
Speaker 6: Don't want to be sued. They can't handle a major breach,
Speaker 6: and startups are able to just move faster given that.
Speaker 2: So every time software stocks sell off with this and
Speaker 2: people say, oh, they micro bargain hunting, and they say
Speaker 2: what's cheap and what baby is being thrown out with
Speaker 2: the bath water?
Speaker 3: Someone always a bunch.
Speaker 2: Of people is like, yes, they look cheap, but have
Speaker 2: you considered a stock based compensation? And it turns out
Speaker 2: that these companies are not nearly as profitable once you
Speaker 2: factor this in. Its very interesting note from Barkleys I
Speaker 2: think it was I think it was Barkleys. This is
Speaker 2: very interesting, and it said our European investors are always
Speaker 2: asking about SBC. Our American investors only ask whether there's
Speaker 2: a crisis.
Speaker 3: I think tells you something about.
Speaker 2: The differentiat in Europeans and Americas. I thought that was
Speaker 2: a fascinating sociological observation. Tell us, like, how should we
Speaker 2: think about the cost, because again, if code generation is
Speaker 2: a cost base, presumably these software companies don't need as
Speaker 2: many employees either. And they could pair back on this,
Speaker 2: So talk to us about how we're thinking about the
Speaker 2: costs inside the software company.
Speaker 6: Great question, and yeah, I mean certainly theoretically true, right,
Speaker 6: but aside from Elon cutting eighty percent of twitter X's headcount,
Speaker 6: we really haven't seen any companies take the pill and
Speaker 6: kind of realize the benefits of that. The SBC debate
Speaker 6: has been going on for a long time. I've had
Speaker 6: it at nausimo of the course of my career. It's
Speaker 6: a real expense. You're issuing your employees stock. They value
Speaker 6: it like cash. Many of them auto sell it the
Speaker 6: day at vests for them. And I think what the
Speaker 6: problem that it creates for software companies is they the
Speaker 6: management teams are addicted to reporting non gap, which excludes
Speaker 6: the impact of SBC. And so if you are an
Speaker 6: entrepreneur who founded the software business who's technical, hasn't really
Speaker 6: ever cared more that much about the financial side your
Speaker 6: product person, you may think that you've been doing a
Speaker 6: good job of being a profitable company because your CFO
Speaker 6: telling you, well, we're at a twenty five percent non
Speaker 6: gap operating margin. That's pretty good when the reality is
Speaker 6: you're running break even, which is a very common state
Speaker 6: of affairs. You know, we looked at the whole universe
Speaker 6: and the media and public software company has a five
Speaker 6: percent non gap net income or gap net income margin,
Speaker 6: which is not enough to value the companies on. And
Speaker 6: so it creates this dynamic where you know, yes, there's
Speaker 6: a terminal value concern, which by far the most important thing,
Speaker 6: but there's also no floor.
Speaker 5: Right.
Speaker 6: I was looking at the earnings report from fresh Works,
Speaker 6: which is a mid market seller of customer support and
Speaker 6: IT management software. It trades at one and a half
Speaker 6: times EVY to sales. If it ran at even a
Speaker 6: ten percent gap margin, it'd be trading at fifteen times earnings.
Speaker 6: You know, that's which is a pretty attractive place to be.
Speaker 6: You could get some value investors, maybe some European investors
Speaker 6: interested in buying it there, but it doesn't have material
Speaker 6: gap earnings. And on their earnings call, there was no
Speaker 6: real you know, sense of trajectory towards that, and you
Speaker 6: see the share price down sixteen p exactly, and like,
Speaker 6: the top line results were actually pretty good, and so
Speaker 6: there's a real issue here on the financial side as well.
Speaker 6: It's incredibly disappointing to me that management teams haven't embraced
Speaker 6: this as a way to cut costs themselves, and I
Speaker 6: expect they will.
Speaker 2: Yeah, talk to us about this specifically. Are we going
Speaker 2: to see big layoffs across the SaaS space in the
Speaker 2: near term and what do you think is the timeframe
Speaker 2: for that great question?
Speaker 6: I think we will. I think we've seen that management
Speaker 6: teams do respond to price signals. If you look at
Speaker 6: the history of the sector, it was in twenty twenty
Speaker 6: three when there was a round of layoffs and companies
Speaker 6: showed margin, and then they've kind of resisted it for
Speaker 6: the last two years. The thing about it is layoffs
Speaker 6: can help you move faster, right. I think if you
Speaker 6: look within any company today, unfortunately, there is this spectrum
Speaker 6: of employees and how fast they've adopted AI, whether they're
Speaker 6: still doing things the old way or they're on cloud code,
Speaker 6: cloud cowork, kind of changing the way that they work.
Speaker 6: And employees who are on the lower end are actually
Speaker 6: slowing you down as a company. They're not even zero
Speaker 6: marginal product, they're negative marginal product. There's just been such
Speaker 6: a change in how you work, especially in software development,
Speaker 6: and so I think management teams are going to realize
Speaker 6: that there's two benefits to actually doing layoffs in addition
Speaker 6: to the obvious pain of it and the kind of
Speaker 6: human costs which I never forget to discuss. But one
Speaker 6: is saving money and showing your shareholders you're financially disciplined
Speaker 6: and probably seeing your share price stabilize, especially if you're
Speaker 6: trading at some very low multiple. And the second is
Speaker 6: moving faster and also almost as importantly, being able to
Speaker 6: pay your top performing employees. The war for talent in
Speaker 6: Silicon Valley has never been more intense right now. Is
Speaker 6: talking to a private company you invested in and they're
Speaker 6: losing employees left and right to these high growth AI
Speaker 6: companies who can afford to pay huge compackages in both
Speaker 6: equity and stock, and you want to keep your good people.
Speaker 5: You don't want it.
Speaker 6: You don't want these AI companies that pluck away all
Speaker 6: the best people and leave you with the folks who
Speaker 6: relative leddites. And so I do think we'll see this.
Speaker 6: It's very sad that that will have to happen, but
Speaker 6: it's the obvious path forward for the sector, and I
Speaker 6: think if done right, it accelerates innovation.
Speaker 4: I have a tangential question on that note, which is
Speaker 4: whenever we talk about technological disruption, you know, people bring
Speaker 4: out examples of like remember when Excel was basically actual,
Speaker 4: people sat down with like papers in front of them
Speaker 4: doing the math. And those people didn't disappear when Excel
Speaker 4: got created, but they started doing new things. I imagine
Speaker 4: a lot of people are very interested right now in
Speaker 4: alternative careers for basic commoditized coders. What do those actually
Speaker 4: look like? Yeah, I feel like you might have some
Speaker 4: insight here the alternative.
Speaker 6: Well, so I think there's two ways to answer the question. Right,
Speaker 6: There's like what do you do if you want to
Speaker 6: stay a coder? And then there's what are the careers
Speaker 6: that are going to still exist over time?
Speaker 5: Right?
Speaker 6: I think if you think about what's happening to coding,
Speaker 6: it reminds me a lot of civil engineering, and so
Speaker 6: it's kind of a funky example, but you know, civil
Speaker 6: engineers used to work pen and paper doing calculus will
Speaker 6: this bridge hold up or not? That's been obsolete for
Speaker 6: a very long time. All those calculations are done by
Speaker 6: a computer. They're kind of clicking and moving, and they
Speaker 6: go on site, they collect some data, they talk to stakeholders,
Speaker 6: and they're effectively project managing this computer that can do
Speaker 6: the physics part of their job. For them, it's important
Speaker 6: that they understand the physics in case something looks strange,
Speaker 6: but they're not doing much physics right. That's clearly where
Speaker 6: software engineering is heading in the near term, and a
Speaker 6: lot of companies that's already there. And these companies are
Speaker 6: still hiring software engineers because that job is valuable, and
Speaker 6: in fact, each individual software engineer is way more productive
Speaker 6: than they were before. And there's happily elastic demand for software,
Speaker 6: Like we still are undersupplied with software in the world,
Speaker 6: and so there's quite a bit of room to go
Speaker 6: to add those and so I'm not necessarily bearish on
Speaker 6: the demand for software engineers, at least for the next
Speaker 6: you know, three to five years beyond that if things
Speaker 6: get weird hard to tell. But then for people more broadly,
Speaker 6: I think the the best advice is just adaptability, you know,
Speaker 6: constantly trying and testing these tools, making sure you're staying
Speaker 6: at the cutting edge of them, and then being aware
Speaker 6: of what's human right. I think in like in my
Speaker 6: work and venture investing, you know, there's a lot of
Speaker 6: data that comes out of human relationships that an AI
Speaker 6: wouldn't have access to you know, an AI can't call
Speaker 6: its friend at another fund and ask how company is doing.
Speaker 6: Not yet, at least they have to make some friends first.
Speaker 3: Right, they're talking about they are talking to each other
Speaker 3: on moultbook.
Speaker 6: Right, they're talking about their Moult book. Yeah, so maybe
Speaker 6: if there's an AI agent from Sequi.
Speaker 3: I was intrigued by that.
Speaker 5: Yeah, it was very evocative, but pretty fake.
Speaker 4: Well also there was that Wired article of the guy
Speaker 4: who like infiltrated as a bought and pretended to.
Speaker 2: Be They're like, oh, why are we let's create a
Speaker 2: new language just for us, Like they're not making new.
Speaker 6: Languages right, But I think I think the rough mental
Speaker 6: model is if there was any effort to outsource your
Speaker 6: job to India, that's risk because that tells you that
Speaker 6: job can be done by person, someone who's not physically
Speaker 6: present in a space. And you know, if you like
Speaker 6: working on problems in isolation, not socially with other people,
Speaker 6: you know, grinding out math problems or little coding assignments,
Speaker 6: that's probably also a pretty tough place to be. It's
Speaker 6: going to be more social world.
Speaker 4: This is something we've touched on before, which makes me
Speaker 4: kind of sad, which is the edge in the AI
Speaker 4: world becomes like sociability right, And to some extent we
Speaker 4: talked about this in the context of look smack, I
Speaker 4: know you love it, Joe, I do not.
Speaker 2: Can I say two little observations from my time vibe
Speaker 2: coding in twenty twenty six that are interesting. One is
Speaker 2: I have zero technical background. I've been surprised by the
Speaker 2: speed with which I can build intuitions about when it's
Speaker 2: going off the rails, like when it's doing something that
Speaker 2: doesn't seem right. Like I joke that vibe coding is
Speaker 2: just typing make it better presenter over and over again
Speaker 2: and then hitting yes. When it offers to do something.
Speaker 2: You actually can start to build an intuition fairly quickly,
Speaker 2: like when this doesn't make sense, and then the other thing.
Speaker 2: And this relates to your question of like trusting the AI.
Speaker 2: So one of the things I'm doing is I'm having
Speaker 2: a lot of documents get annotated, and that I do
Speaker 2: that through the Claude API, which actually runs up the
Speaker 2: bills a little bit. And one thing this API run
Speaker 2: was gonna cost like one hundred dollars, and I stupidly
Speaker 2: asked Claude it is like, is this a good thing.
Speaker 2: It's like, well, when you're done with this API API run,
Speaker 2: you're gonna have this annotated asset that no one else
Speaker 2: has done, and that'll be very.
Speaker 3: It was sort of useless what I did, so you shouldn't.
Speaker 5: It's selling it.
Speaker 3: Selling itself is like, oh.
Speaker 2: Yeah, use the API, Joe run this like annotated all
Speaker 2: these documents. It wasn't actually like a good use of
Speaker 2: my time. So you can't really always they're just gonna
Speaker 2: they're gonna just sell these things. So Tracy asked about data,
Speaker 2: and there's one other sectors that I'm interested in.
Speaker 3: You see these companies like.
Speaker 7: Moody's or fair Isaac.
Speaker 2: Or sp Global they have an index, yeah, and they're
Speaker 2: getting they're selling off too. And it's not like this
Speaker 2: is another area where like it's you know, people were
Speaker 2: fund managers for a long time. Unless things get really weird,
Speaker 2: the super they are gonna be like benchmarking themselves off
Speaker 2: of like that S and P five hundred for a
Speaker 2: long time, or lenders are gonna be using the FICO
Speaker 2: for a very long time, et cetera. Intuitively, it would
Speaker 2: strike be is this would be a very hard thing
Speaker 2: for ad or replace.
Speaker 6: I share your intuition, right, I can't say I fully
Speaker 6: understand the selloff and these companies, I wonder if there's
Speaker 6: not some parts of their businesses that are more services
Speaker 6: or consulting heads that people are there's often like combinations
Speaker 6: right where like I don't think anyone suspecting that, like,
Speaker 6: you know, the S and P five hundred is going
Speaker 6: to be displaced as an index throughout Yeah. Maybe, but look,
Speaker 6: we're in a world where folks are very happy to
Speaker 6: shoot first and ask questions later once AI risk comes out.
Speaker 2: Actually going back to your hedge fund time, like how
Speaker 2: much is it just the sort of the nature of
Speaker 2: hedge fund traders right now where there's very little stomach
Speaker 2: to take any downside risk and appear to look stupid
Speaker 2: for missing you know, yeah the bag here, and how
Speaker 2: much do you think that's contributing to some of these
Speaker 2: market money?
Speaker 5: It's a great question.
Speaker 6: I won't speak to my fund because I think CO
Speaker 6: two and Tiger cubs like it are a fairly small
Speaker 6: share of the overall market and dollars, right, but if
Speaker 6: you look at trading volumes, the pod shops, Citadel, Ballyas
Speaker 6: and the millennium are very large share of the volumes,
Speaker 6: and yeah, those people can't afford draughtouts, right. And the
Speaker 6: scary thing about this for them is because it's not fundamental.
Speaker 6: Because the companies aren't struggling themselves, they have no idea
Speaker 6: when it will stop, right, And so you're left predicting
Speaker 6: this thing. And you're like, well, I bet my career
Speaker 6: that people are going to feel better about software companies
Speaker 6: in three to six months than they do right now.
Speaker 6: And you know, your one open AI model release or
Speaker 6: anthropic model release away from more fear. And so I
Speaker 6: do think there's a lot of short termism right now,
Speaker 6: and there's all but again I think we come back
Speaker 6: to the SBC point. But there's also no valuation support,
Speaker 6: no real valuation support. You know, in normal times, if
Speaker 6: companies were like this, they'd be buying back a bunch
Speaker 6: of their stock, taking their share out, issuing dividends. You know,
Speaker 6: I have a friend who works at a mutual fund
Speaker 6: with is a lot of dividend funds that would love
Speaker 6: to buy dividending companies growing ten to fifteen percent, like
Speaker 6: a lot of software companies. But they're not right, and
Speaker 6: so you've kind of lost the ability to kind of
Speaker 6: put an actual floor in underneath evaluations.
Speaker 2: As a result of that, Jared Sleeper, thank you so
Speaker 2: much for coming on odd Laws and explaining how socrow works.
Speaker 5: My pleasure is super fun.
Speaker 7: Thanks Tracy.
Speaker 2: I thought that was really interesting. I'm very fascinated with
Speaker 2: this idea that in the short term most of these
Speaker 2: businesses are doing fine. In the long term it might
Speaker 2: go to zero. But also with the short term they're
Speaker 2: not really doing fine because actually, you on a gap basis,
Speaker 2: they're not making much money. I guess that sort of
Speaker 2: makes sense that they're all selling off right now.
Speaker 4: Yeah, I keep thinking this is probably a stretch, but
Speaker 4: I keep thinking back to that book Full Jobs. Yeah, remember,
Speaker 4: and like the argument there is a lot of jobs
Speaker 4: exist not because like people are doing anything specific, but
Speaker 4: because they're like providing some sort of social value in
Speaker 4: a way. So, for instance, like you have a person
Speaker 4: who is essentially the designated scapegoat for senior management. And
Speaker 4: I keep thinking of you know, business is basically an
Speaker 4: ecosystem of different players, So it might be that in
Speaker 4: the new AI world the role of software companies just
Speaker 4: kind of changes, like their social role changes. And I
Speaker 4: don't know what the price or the valuation looks like
Speaker 4: on that it.
Speaker 2: Still feels like to me, I have no I was
Speaker 2: going to say something about how businesses we're going to
Speaker 2: what do I know? I have no idea how businesses
Speaker 2: are going to buy software in the future. I did
Speaker 2: think that was really helpful, Like I really don't know
Speaker 2: anything about how the software business works generally, so I
Speaker 2: find that very helpful. One other interesting thing though, and
Speaker 2: it may sort of speak to think about this risk,
Speaker 2: is just the idea that, like, even high end software
Speaker 2: is not that much money, right, So if you have
Speaker 2: a salesperson who's making two hundred and fifty thousand dollars,
Speaker 2: what is one thousand dollars a year from salesforce.
Speaker 7: To do their job?
Speaker 2: Particularly and then also given the fact that you know,
Speaker 2: free and open source software has existed for a long time,
Speaker 2: but still you know you want to pay an implementer
Speaker 2: for a company that like manages et cetera. Getting from
Speaker 2: like here to there where okay Ai really changes the
Speaker 2: nature of software buying does feel like you have to get.
Speaker 3: Into this is going to be weird toward tear, but maybe.
Speaker 7: Things are good.
Speaker 3: I think things probably are going to get really weird.
Speaker 4: Yeah, I think that's that's a pretty good bet. Right, Like,
Speaker 4: if you bet on weirdness, if there is a weirdness index.
Speaker 3: Someone should build that weirdness index.
Speaker 4: That would be a pretty good investment. Yeah, all right,
Speaker 4: shall we leave it there, Let's live it there. This
Speaker 4: has been another episode of the All Thoughts podcast. I'm
Speaker 4: Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Joe Why Isn't Though? You can follow me
Speaker 2: at the Stalwart. Follow our guest Jared Sleeper He's at
Speaker 2: Jared's Sleeper. Follow our producers Carmen Rodriguez at Carmen armand
Speaker 2: dash Ol Bennett at dashbod and Kilbrooks at Kilbrooks. For
Speaker 2: more odd Laws content, go to Bloomberg dot com slash
Speaker 2: odd Lots or of a daily newsletter on all of our.
Speaker 7: Episodes, and you can chat about all.
Speaker 2: Of these topics twenty four to seven in our discord
Speaker 2: Discord dot gg slash odd.
Speaker 4: Lots And if you enjoy odd Lots, if you like
Speaker 4: it when we talk about the casastrophy Casastrophe, then please
Speaker 4: leave a positive review on your favorite podcast platform. And remember,
Speaker 4: if you are a Bloomberg subscriber, you can listen to
Speaker 4: all of our episodes absolutely ad free. All you need
Speaker 4: to do is find the Bloomberg channel on Apple Podcasts
Speaker 4: and follow the instructions there. Thanks for listening.