B (2:32)

And all you have to do is just say AI industry. And there's a. You know, it's really, there's a lot of anxiety. But there's something that, like, doesn't make any sense to me about this or the thing that I'm wrapping my head around is like, sure, any of us could easily like write some software, but like writing software is a cost center for these companies. Right. If you're Salesforce and you can trivially reduce the cost of building software, that should. That's also a benefit for you. And there's a lot more to a software company than just code generation because there's all kinds of, you know, network effects and links into this. It's like a software company is clearly more than just code. And so the fact that maybe code can be generated a lot cheaper does not scream to me like, oh, these companies are worth less than they used to. Sure.

C (3:14)

But at the same time they've been pricing. Their pricing is based on that assumption. Right. Like that there is no competitor for what they're doing. And suddenly you might have an in house competitor.

B (3:25)

Absolutely. But you know, it's like network effects. And do companies want to start like building their own, like payroll software? Anyway, I have a lot of questions about this sell off. And to your point.

C (3:35)

No, no, no. This is you doing like penance first, causing the sell off.

B (3:42)

All right, let's talk to someone who actually might be able to answer some of these questions for us. We're going to be speaking to someone who's been in the software space, an investor in the software space for a long time, recently put out a great deck, really diving into SaaS of the SaaS apocalypse. And what kinds of companies are thriving and what kinds of companies were struggling even before everyone started talking about AI, code generation and all that. We're going to be speaking with Jared Sleeper. He is a partner at Avenir, which does a growth investing private company. So Jared, thanks for coming on odd lots.

D (4:12)

Yeah, my pleasure. Excited to be here.

B (4:14)

Why are we talking to you? Just, you know, for our listeners. Apparently this is your first time on a podcast, which is crazy, but why are we telling you? Give us a little bit about your background investing in software and understanding the space.

D (4:23)

Yeah, my pleasure. So I think one thing that makes me a little bit different in the investor world is that I've spent time investing in early stage startups, public companies and everything in between. So I spent a chunk of my career at an early stage venture fund in Boston called Matrix Partners, working with an OG SaaS investor named David Scott, and then was also at CO2 where I ran public software. And so I kind of have this like experience across the spectrum from ground floor startups to looking at the big public companies, which I've done for the last 10 years.

C (4:51)

Perfect guest, Perfect guess. So give us some color on the mood in software at the moment. Are people like, I don't know, hunkering down in their bunkers? How bad is it?

D (5:00)

Yeah, I get texted constantly from folks on the buy side just, you know, retrenching. I can't believe this is happening. Can it go lower? I keep saying that. It's the hundredth time I bought the dip. You use the Sasspocalypse like Cassastrophe is my Cassastrophe.

C (5:14)

That's my.

D (5:16)

It's definitely one of those moments. And we were talking about this a little bit earlier before starting, but one of the things about software that's really fascinating is there's very few folks even on the buy side who really understand how software works. It's one of those Rorschach test kind of sectors where almost no one's logged into Salesforce and clicked around, much less been a Salesforce admin and understood the full complexity. And so when there's panic, there's not a lot of support for the stocks and people, you know, get scared very easily.

B (5:43)

We explain what this means. So for example, in a lot of companies, it's like you're saying that the people who invest or trade these stocks, they just know them as financial tables basically and they have some idea of their financials and some idea of their customer base, et cetera, but they don't have like a great intuition for the product. Unlike say people who use Instagram and therefore might have a feel about Meta, for example.

D (6:09)

Yeah, if you're an investor in Lululemon, you have a pretty solid conception of what that business is.

C (6:13)

You can go in, see through yoga pants.

D (6:15)

Exactly. You can buy the product, ship it to yourself. If you're an investor in Viva, which makes CRM software for pharmaceutical reps, I bet you there's almost no investors in Veeva who have ever been inside the product even once, much less used it on a day to day basis and understood how it works.

C (6:31)

So I'm going to go way back in time and start at, I guess the very beginning. But why is it that software like this, you know, payment management systems, whatever, why were they historically not developed in house? Like how did we get this model where we have these huge software companies that are really, to date have been really integral to a lot of businesses?

D (6:52)

Yeah, it's a great question. Back in the very early days of software, like back in the 70s or 80s, there was a lot done in house and we've seen a very clear mix shift over time towards using third party software. And what it comes down to is the software was expensive to build and maintain and there's this need for an ecosystem of integrations around it which are also expensive to build and maintain. And so if you look at a software company, it can afford to have 1, 2, 3,000 engineers, plus partnership teams, et cetera, all working to build the perfect piece of software for a given application. And then what's striking, and this will come up a lot more in this conversation, is not selling it for that much money. Right. A lot of software companies report a stat which is the share of our customers that pay us more than $100,000 a year. And $100,000 a year is less than half of the fully loaded cost of a software engineer. Right. And so the software model was build a product that can be applied to thousands of customers and it's the same product for every customer, and then sell it to them for way cheaper than they could ever hope to build it themselves, even less than the cost of one employee.

B (7:56)

I'd love to just talk long term software history even before. You know, we think a lot about SaaS and these startups and stuff like that, but like a lot of the big companies that we think of in software, especially like Pre Salesforce, whether it's like SAP, Oracle, Microsoft, obviously aren't there a bunch of third party companies whose job is to just like help install it for you, like SAP install and that'll be a totally separate company because it's so big and it's so unwieldy and complex that you actually, you can't just like install it yourself or it has to be customized or whatever, a hundred percent.

D (8:29)

And there's two parts to that which I think are important. One is the integrations into your existing systems. Right. A lot of big old companies have old databases, old app applications, and it's important for everything to be stitched together. So you need software engineers and you know, consultants to go in and understand those existing systems and kind of get them linked up to the new systems. But the other one, which is probably bigger, is just people management and change management. You know, any software system is the combination of the code and all of the individual users who have learned how to use it. If you're trying to change out your CRM at a company, that means training every single sales rep on how to use the new CRM. And getting it right. And if they get it wrong, then you lose deals that quarter. And so, you know, one of the kind of tropes in investing is if you see a company that's doing an ERP transition. ERP stands for Enterprise Resource Planning. It's the kind of core software accounting, you know, supply chain, et cetera. That company's probably going to miss its earnings over the next one or two quarters because those transitions are so painful. And so yes, there's a big consulting complex around it that does its best to come in and parachute in the talent that's required to make those transitions smooth. And that tells you something about what makes software so sticky, or at least has historically.

C (9:42)

It's third party agents all the way down, I feel. But actually on this note, so we hear the integration point brought up a lot and I think the very first episode we did on Claude code, we talked a little bit about it as well. But if you have something like Claude code where you can just give it permissions to make changes to your computer, does some of that integration expertise actually start to go away? Because presumably we are going to get AI, I would assume at some point, given the rate that it's developing and improving, that will be able to do this, like plug itself into various systems.

D (10:17)

Yeah, 100%. I think the challenge of writing the code for the integrations is going away. That's not the bulk of the challenge for majority of integrations. It's about really deeply understanding the prior system and how it maps to the new system. And the reality is within most organizations that's a human problem. It's hey, this column says Status 2004. What does that mean? Like how does that map to the new system that we're building? So you have to go talk to someone and understand it. And so there's certain types of integrations where I think they're effectively solved problems now because you can write a quick, you know, write into, chat into cloud code and get a perfectly written piece of software to make it happen. And then there's others that are just fundamentally human problems because the data doesn't exist in digital space.

B (11:02)

Let's talk more about that because really it is pretty extraordinary the degree to which I don't know the working code. I don't know if it's high quality code, but certainly these models can generate working code and it blows my mind whenever I use it. But talk to us a little bit more about from the perspective of a various software vendors and I'm sure there's a range about what they're selling and how much is it code versus how much is it other stuff and which ones are more exposed to the pure like code generation ability?

D (11:34)

Yeah, it's a great question and you're 100% right. It's producing working code and frankly it has been for the last year or so. I built my first lovable app that was working in production about, about a year ago and it's even intensified in the last three months. Right. I think when people buy software, there's a set of things that they're buying. One thing that I think is important for everyone to understand is that open source software has been a thing. And there have been free open source versions of almost any software you could buy for all of recorded history. There's actually some companies that are public that built their businesses packaging that open source software and adding a few custom features and then support on top of it. Because when a company's reliant on an open source database or a company like Elastic with its elasticsearch product, which is an infrastructure tool, and it breaks, they need someone to call, both for CIA reasons and because it can be very complex and technical and they need to quickly understand it. And so that has been a big part of the story historically is that need to, you know, have support. Another thing that you sell when you sell as a software vendor is what I call herd familiarity, which means everyone on earth knows how to use your software, which just simplifies the training and onboarding workflow. I'll give a few examples because I'm sure it's a new term for listeners since I made it up. You know, zoom is a great business. Microsoft has been giving away a free version of the product forever in teams. Why do people use Zoom? Because in certain industries, almost everyone knows how to use Zoom. They have their Zoom set up, they have their virtual background chosen. They're not going to fumble around for the first minute or two on the call. And, and that's well worth the $20 a month to have a Zoom plan. But that applies to lots of other areas as well. So think about Microsoft Excel, for example. You might be able to use Google Sheets to do the same thing. But do you really want to retrain every person who comes in on the Google Sheets shortcuts versus the Excel shortcuts? It's not a good use of time, especially when the software is already so cheap. And so that's another plank in what people are buying when they buy software is the standardization and the knowledge that they'll be able to hire employees who have that. And then there's things like brand again, the kind of ecosystem that comes around it. And so it really is more than just the raw code.

C (13:43)

We've been joking about this, but the idea of software companies value lying in being a scapegoat essentially for when things go wrong is kind of funny and dystopian, I think, in many ways.

D (13:54)

Yeah. I mean, I think, you know, it's a real fear. Right. And the way I think about it is there are two arguments against software right now. One is the world is going to stay the same, but software is just going to get a lot cheaper over time now that it's cheaper to build. And I think there's no one who would argue that it's not gotten dramatically cheaper to build for reasons that we laid out in our deck. And we can talk through it more. We don't buy that argument. I don't buy that argument. But the second is the world's about to get really weird and the way that knowledge work happens is going to change. And if we think out three, four, five years, who knows if there will even be customer support reps or sales reps or software engineers. And I think that's what's causing the kind of hit to the share prices lately is this terminal value concern.

B (14:35)

Yeah, it was interesting. So one of the companies that's been associated with the. What'd you say? Cassastrophe, One of those companies that's been caught up in this Blue Owl, the private investing firm, Private Credit. I read through their conference call and their CEO was like, not only do we not see red lights, not only do we not even see yellow lights, we actually see a lot of green lights, which I think is really interesting because it can fit with this idea of this year could be fine, next year could be fine, the year after that could be fine. And then the year after that could be zero. Or at least that's the anxiety that there is this terminal value.

C (15:09)

Talk more about there's like a cliff risk.

B (15:11)

Yeah. That there's this cliff.

D (15:12)

Yeah. I think it's really helpful. You know, this is our second iteration of the deck. And so we kind of force ourselves to recenter on what actually happened since the last deck. Right. And there's a very clear pattern in software and what happened over the last five years, which is the pandemic. People freaked out at the beginning, but it was rapidly clear that it was an accelerant for SaaS as everyone tried to digitize their companies. And so you had a spike in the growth rate and net retention of the businesses. It peaked at just over 40% in 2021 for the median software companies. That's really nice annualized growth. And then there was a hangover and that slowed down. And we wrote 18 months ago that that reflected the sector sort of maturing. The adoption had just slowed down because most folks had adopted the software that they needed under the pressure of the pandemic. And so for the last few years after that, we saw this degradation in growth rates across the sector. By the beginning of last year, the median company was growing 18% instead of 40%. You saw a pretty significant drawdown. What's fascinating is that if you look at the actual financial performance of the companies in the last year, it's been pretty good. That growth rate has held. It was 18% again in Q3. Net retention has also been consistent at about 110%. So that's revenue from existing customers over the same revenue from those customers the prior year. So there's not a churn issue developing or a lack of expansion within the customer base. And a lot of the companies are actually accelerating growth or guiding to accelerating growth. We have a chart showing the number of those companies has increased each quarter of the last three successive quarters. And so there's a lot going on right now with the terminal value. But it's very hard to argue that this is something that's happening today and showing up in the numbers. The thing is, investors are sharp, right? And they're constantly looking for, looking. Yeah, I mean, look at Chegg, right? Which went down very quickly in the aftermath of ChatGPT coming out. And that was completely correct, Right? Investors were ahead of that. And of course, for the first few quarters, the management team of Chegg, you know, had their heads in the sand. But then it became clear that it really was existential to their business.

C (17:08)

That's a fun chart.

B (17:10)

I thought I was looking at a typo because I saw, wow, that was. That was a near $100 stock in February 2021. It is now a 61 cent stock. That's rough.

D (17:20)

And you have to give the markets credit. Like the second chat GPT came out, people were like, this company's in big trouble. They didn't wait for it to hit the financial results. And so there is signal in what people think.

A (17:44)

Ukg, their HR pay and workforce management tools help business leaders empower their people. Because when work works, everything works. Learn more@ukg.com work I have a bunch.

C (17:57)

More questions, but just briefly, where does data actually fit into all of this. Because the other thing we hear about AI is maybe the models don't matter that much, but it's the actual data that you have access to. And I imagine the customers themselves of SaaS companies, they have their own data. Do the SaaS companies have their own data as well? Can they build off of that?

D (18:18)

Yeah, it's a great question. And we're here at one of the world's biggest data companies, so very apt.

C (18:23)

Full disclosure.

D (18:24)

Data is definitely something that gets more valuable in this world. If you think about a stylized AI model, it could have PhD level intelligence in a domain, but if you hired a PhD into your company and sat her down on her first day, she wouldn't be very useful. Right. She would have to understand how the organization functions, where do things live. Do I trust this chart or that chart? I need access to the Google Drive. I need access to Slack. I need to spend some time reading up. And so we call this kind of context, right? It's all the extra information that an AI needs to get something done, no matter how intelligent it is. And we wrote about this in the chart in the deck. But there's a real question of who becomes that system of context. And you're right. A lot of the software companies do sit on a pool of very important data. Let's talk about Salesforce, for example. Right? CRM is where you track the records of every customer you have, every prospect in your pipeline, all of your historic interactions with them. Notes from sales reps on what's going on, the status of their account, their customer support requests. It's an incredibly complex piece of software for a large enterprise. And obviously if you are an AI agent working within a company, you would need access to that in order to get almost anything done. Right. But you need more than is there. You don't know what happened at the sales dinner last night unless the rep took really detailed notes. And I can tell you one common learning in software is they do not take very detailed notes, especially at a sales party.

B (19:49)

Right?

D (19:49)

Yeah, exactly. People assume that software management teams know exactly what's going on, but they're looking through really messy Salesforce data and doing their very best.

C (19:56)

Now I'm imagining a sales agent being like, the Cabernet was exquisite at last night's party. Just putting in all these irrelevant, like diary entries.

D (20:05)

Exactly. But a lot of that context does live in human brains. You know, a sales rep meets a person at dinner, gets to know their kids, figures out what sports team they root for, and they're not Automatically pumping all that into the CRM. And so there's this race to collect the information that an AI agent would need in order to actually take proactive action. And the software companies have a position there. But there's also this set of AI native startups that are coming in, building actual agents who are doing their own work to collect that context. And that's one of the battles that we saw that we kind of highlighted in our deck is whoever wins that has a chance to be a really valuable company.

B (20:42)

You know, I think about, and I think you talk about this in your deck, but when I think about software I sort of have like if there's a spectrum, you know, I think about salesforce.com which is a platform and there's third party developers that build on top of Salesforce and they sort of offer everything. And then I think about something niche like this is the company that makes point of sale software for dentist office and they went around by giving them free payment terminals and they joined Y Combinator and you know, they signed up 10,000 dentist office and then they pay those offices, pay them $10 a month forever for access to that, you know, whatever. I'm just making it up. But things like that. Is there a side of the spectrum that's more at risk here? Is that spectrum legitimate way to think about the industry or is there threats on sort of wherever you look?

D (21:32)

Yeah, it's a great question. I mean certainly in the world gets really weird scenario, it's not clear there's anywhere immune from threats. But it's important to think through what it looks like. I think what's most at threat is companies that serve enterprises with very customized software already or software that takes a very heavy implementation. And the reason is if anyone's going to take advantage of this wave of technology to really advance and replace a core system of software, it's going to be the enterprises that have the resources and the customization needs. If you think about SMBs, my dad runs our family's grocery store. It's been in the family for 100 years. And he just changed his point of sale for the first time in a few decades. And it was a really messy process, took a long time.

C (22:18)

Will your dad come on opals?

D (22:19)

Yeah, yeah, sure thing.

C (22:20)

We love grocery store.

D (22:21)

We'd love to do that episode all about independent grocery. And you know, he's certainly not going to sit down and vibe code himself a point of sale system and put the store on it. I can guarantee you that. Nor will any dentist. Right. There's A chance that someone comes along with a cheaper version. But you know, I think that's not something he's going to switch to anytime soon. He's not. He doesn't want to go through that pain for another few decades to come. Right. And so it really is, you know, kind of company by company. Like I'm doing this exercise right now on X, where every day I look at a different software company and just think hard about what will AI look like for this company. And it's really interesting when you press, I'll give an example like DocuSign, which I think to most investors would seem like an incredibly simple, easy piece of software. Right. It's an E signature software. We've all experienced it. DocuSign has more employees today than OpenAI and anthropic combined.

B (23:13)

Oh my God.

D (23:14)

Which is a crazy stat and probably reflects that labor is inefficiently allocated across the market. But. But when you actually double click into what DocuSign does, there are ways in which it's very complicated. Right. Understanding the signature regulations in every country around the world. What does it take for a signature to be legally valid? Most of its signatures are done as an API, so folks are integrating it into their own applications. And there's a benefit to using DocuSign, which is the brand. People have been giving away free E signature software for a very long time. But if you're a company of a certain esteem, you want to make sure your customers trust what they're signing. And if they're getting a contract from you, you'd much rather say DocuSign than XYZ. Sign that someone vibe coded. Right. And so I think it's really important to look company by company. It's definitely a stock picker's market where there's some that are either relatively immune or have a chance to benefit and there's others that could be in real trouble.

C (24:08)

So is the argument the bull case for software or at least the non sudden death case for software? This idea that like, okay, if you have a software company that's producing, I don't know, like DocuSign, you're able to sign documents digitally and track them and share them and all of that, you can build more quickly and more efficiently off of that base model and provide like new versions, new customizations for customers. So I could do DocuSign for dentists just to stick with that example. I don't know what specific needs dentists would have. I don't know, maybe marking up like teeth or something. Yeah. And then I can do like Docusign for doctors and Docusign for sales agents or whatever and just keep going.

D (24:52)

Yeah, I think that's right. I actually kind of think of it as there's three cases, there's the software gets wiped out case, there's the not much happens to software case, and then there's the bull case where the software companies capture a lot of value. I think it's a little different than them adding a lot of features and functionality, frankly. I think a lot of software products today are pretty mature. There's been thousand engineers working on them for 10 years. And they've built not all, but most of the things that you'd want to build with today's technology. But with agents there's ways to automate a big chunk of the work. So one software company that's done this very well is Intercom. Intercom sells customer support software. It's those little widgets on the bottom right hand corner of websites. They were the creators of that. They had a nice business. But then they got very aggressive about building out an AI product called Fin, which answers customer support queries on its own. And I think they've mentioned that it's almost 100 million of ARR now on a base that was like 300 million of ARR or something like that. And so they've really re accelerated their business by building an AI native tool that actually solves the work. Not just a tool that not just kind of exists as a tool that humans use. And so yeah, I think that's like the mega bull case. Right? I think about it like almost a transition from brick and mortar retail to E commerce where you have a brand new way of doing business and you have a bunch of legacy companies and some of them will probably just exist as they always have. Others can benefit from the change and add new business lines. You look at Walmart's share price, it's done amazingly well at incorporating E commerce into its business. And then there's going to be some that are like Sears and go away.

C (26:29)

That's funny, Sears always reminds me. My dad loved Sears because he always said the parking lot was empty when he goes to the shopping mall. So he always went through Sears anyway. So I understand the cost argument. It brings down the cost of code. Maybe you have fewer employees or whatever, but where does growth actually come from in that world? How are you expanding your customer base?

D (26:51)

Yeah, you're really going to them and saying we are replacing human labor and there's a different pricing paradigm now. You used to think of us as something you paid 20, 30, 40, $50 per seat per month for, as a tool for your employees. Almost as if, you know, your employees are artisans and they're getting a toolkit to work with. And now we're just selling you an employee or the results of an employee. So, you know, we will sell you customer support tickets, getting closed out for 50 cents or a dollar per ticket. And you can do the math of what it would cost you for the human to do that or what it would cost you for AI to do that. And we'll be cheaper, but we're also dramatically increasing what you pay us because, you know, we're cutting into a completely different stream. And so that's what I think it looks like. We see a lot of exciting examples in the startup space of companies that are getting much, much higher pricing.

B (27:44)

And this is a totally new pricing model for software. We actually just recorded another episode and they the guest teased at that. But talk to us about just like results based pricing. Talk to us.

D (27:54)

Yeah, it's results based pricing. There's a lot of questions on how it'll ultimately shake out. Fundamentally, what these companies are doing is they are reselling intelligence, right? The core model vendors, OpenAI, Anthropic, Google, have created a way to get elastic intelligence. And if you have the right data and you can put the right harness around it, you can now sell that to your customers. What's an open question is how do you price that relative to the intelligence? So I was talking to someone this morning who said they think 50% gross margins on intelligence are about right. But we see a lot of variance in how startups are doing it today. Some are getting 80% gross margins on top of the model vendors, others are getting 20%. But what's absolutely true in any case is if you're able to do that, you get much, much higher pricing in total dollars than you did before. Orders of magnitude in some cases.

C (28:44)

But just to be clear, like the cost savings, it can't be priced so high that the company that's using the software to produce these outcomes, like, isn't saving money. Right? That's the balancing act, 100%.

D (28:57)

But I think if you think about when we talked about this a little bit earlier, think about where software pricing was already right. You know, think about Salesforce, you know, at the elite tier, you know, 80, 90, $100 per user per month. So for round numbers, say $1,000 per user per year for sales reps who could be making on average 250, $300,000 per year, if you have a technology that can come in and replace a sales rep, you can charge $50,000, still give the customer a 5x ROI, and then you've effectively 50x'd your take rate on that revenue. And so that's the exciting opportunity that has people excited in startup land, for sure. If you talk to folks from Silicon Valley, they are foaming at the mouth about the opportunity to really expand tech spend in this way. And that's also the opportunity for the software companies that get it right.

B (29:44)

There must be another risk too, which is that if you could sort of resell intelligence at say an 80% gross margin, then for the model makers themselves, they're like, well, why do we just want to be. This is going to sound weird. Why do we want to be the dumb intelligence? Right? And that's sort of like we don't. They used it. We don't want to be the dumb pipe. And we saw that like in the cloud era, right? The Azures and the Google Cloud and the Amazon one, they didn't want to just be commodity cloud and they started building like, medical features. They wanted to differentiate themselves. So it must be a risk for the companies reselling intelligence that it's so lucrative. And then. And like, how are you thinking about the core model makers themselves and how they're thinking about expanding into some of these fields rather than just piping in intelligence for them?

D (30:30)

Well, look like in any situation they're gonna have to make decisions, right? So when Amazon built aws, they had to decide where are we gonna press and where are we not? Are we gonna sell database software or are we gonna let other vendors do that on top of us? They kind of made those decisions as it went out. What's really interesting is if you look at the foundation model vendors, they have been racing towards the application layer. Both Claude code and cowork and OpenAI codecs are applications that people download and use. Right. And I think that reflects this understanding that there is value in getting the users used to using your application. Otherwise you risk being an API that's commoditized, people switch back and forth between you and that kind of application vendor has that control.

C (31:30)

So one of the advantages that software has is like this network effect comfort software as a security blanket for management. Right. But at the same time, people are getting really comfortable with AI telling them everything. And I keep thinking like, if part of the sales pitch for software is this sense of comfort, but then AI is rapidly becoming the thing that you talk to for everything, does it eventually just Become a portal for doing all these different things.

D (32:04)

It's a really interesting question. And this is where there's probably the biggest disparity between how enterprise buyers think and, and how humans think. Right. I'm sure you guys have seen claudebot and the kind of rise of this, you know, open source agents that people are deploying for themselves, giving them access to everything, their whole computer, etc.

C (32:22)

That's Joe.

B (32:23)

Yeah, no, I didn't install, I didn't install Claude.

C (32:26)

Oh, you didn't know?

D (32:27)

I'm getting mine set up mini with a hammer next to it.

C (32:29)

Well, I'm really curious why not because of this issue.

B (32:32)

Yeah, because of that. And just seemed like a potential waste of tokens and stuff.

D (32:37)

Yeah. And then, then it turned out that for a while on multiple book, which was the social media for all of the, all the APIs were available in a public facing database that anyone could go read. And so it was like a completely open system that had to get fixed. And so enterprises really do worry about this stuff and they worry about it for good reason. I'll give you another really interesting example. So there's a bunch of startups that help you record zoom calls and transcribe them. All of those zoom calls then become legally discoverable because they're transcribed somewhere. And so you have VCs in Silicon Valley who will refuse to use them and you have other firms that are all in and recording everything that happens across the board so that they can upload that into AI as context. I think it's a really, it's a really great point. You know, and one of the things that makes me wonder is companies that are willing to skirt the rules or, you know, play fast and loose will be moving much faster over the next two or three years. And one of the reason big incumbents struggle is because they actually do have to care about this stuff. They have stuff to protect. They don't want to be sued, they can't handle a major breach and startups are able to just move faster given that.

B (33:40)

So every time software stocks sell off with this and people say, oh they might go bargain hunting and they say what's cheap? And what baby is being thrown out with the bathwater? Someone always, a bunch of people is like, yes, they look cheap, but have you considered stock based compensation? And it turns out that these companies are not nearly as profitable once you factor this in. It was a very interesting note from Barclays. I think it was, I think it was Barclays. This is very interesting. And it said our European investors are always asking about sbc. Our American investors only ask when there's a crisis, which I think tells you something about the difference between Europeans and Americans. I thought that was a fascinating sociological observation. Tell us like how should we think about the cost? Because again, if code generation is a cost base, presumably these software companies don't need as many employees either and they could pare back on this. So talk to us about how we're thinking about the costs inside the software company.

D (34:34)

Great question. And yeah, I mean, certainly theoretically true, right? But aside From Elon cutting 80% of Twitter X's headcount, we really haven't seen any companies take the pill and kind of realize the benefits of that. The SBC debate has been going on for a long time. I've had it ad nauseam over the course of my career. It's a real expense. You're issuing your employees stock, they value it like cash. Many of them auto sell it the day it vests for them. And I think what the problem that it creates for software companies is they the management teams are addicted to reporting non gaap, which excludes the impact of sbc. And so if you are an entrepreneur who founded a software business, who's technical, hasn't really ever cared that much about the financial side, you're a product person, you may think that you've been doing a good job of being a profitable company because your CFO is telling you, well, we're at a 25% non GAAP operating margin. That's pretty good when the reality is you're running break even, which is a very common state of affairs. We looked at the whole universe and the median public software company has a 5% non GAAP net income or GAAP net income margin, which is not enough to value the companies on. And so it creates this dynamic where yes, there's this terminal value concern, which by far the most important thing. But there's also no floor. I was looking at the earnings report from Freshworks, which is a mid market seller of customer support and IT management software. It trades at one and a half times EV to sales. If it ran at even a 10% GAAP margin, it'd be trading at 15 times earnings. You know, that's which is a pretty attractive place to be. You could get some value investors, maybe some European investors interested in buying it there, but it doesn't have material gap earnings. And on their earnings call there was no real, you know, sense of trajectory towards that. And you see the share price down, it's down 16%. Exactly. And like the top line results were actually pretty good. And so there's a real issue here on the financial side as well. It's incredibly disappointing to me that management teams haven't embraced this as a way to cut costs themselves. And I expect they will.

B (36:40)

Yeah. Talk to us about this specifically. Are we going to see big layoffs across the SaaS space in the near term? And what do you think is the timeframe for that?

D (36:49)

Great question. I think we will. I think we've seen that management teams do respond to price signals. If you look at the history of the sector, it was in 2023 when there was a round of layoffs and companies showed margin and then they've kind of resisted it for the last two years. The thing about it is layoffs can help you move faster. Right. I think if you look within any company today, unfortunately there is this spectrum of employees and how fast they've adopted AI, whether they're still doing things the old way or they're on cloud code. Claude cowork kind of changing the way that they work. And the employees who are on the lower end are actually slowing you down as a company. They're not even zero marginal product, they're negative marginal product. There's just been such a change in how you work, especially in software development. And so I think management teams are going to realize that there's two benefits to actually doing layoffs in addition to the obvious pain of it and the kind of human costs, which I never forget to discuss. But one is saving money and showing your shareholders you're financially disciplined and probably seeing your share price stabilize, especially if you're trading at some very low multiple. And the second is moving faster and also almost as importantly, being able to pay your top performing employees. The war for talent in Silicon Valley has never been more intense. Right now I was talking to a private company invested in and they're losing employees left and right to these high growth AI companies who can afford to pay huge comp packages in both equity and stock. And you want to keep your good people, you want these AI companies that pluck away all of the best people and leave you with the folks who are relative Luddites. And so I do think we'll see this. It's very sad that that will have to happen, but it's the obvious path forward for the sector and I think if done right, it accelerates innovation.

C (38:35)

I have a tangential question on that note, which is whenever we talk about technological disruption, you know, people bring out examples of like, remember when Excel was basically Actual people sat down with like papers in front of them doing the math. And those people didn't disappear when Excel got created, but they started doing new things. I imagine a lot of people are very interested right now in alternative careers for basic commoditized coders. What do those actually look like? Yeah, I feel like you might have some insight here.

D (39:10)

The alternative. Well, so I think there's two ways to answer the question, right? There's like, what do you do if you want to stay a coder? And then there's what are the careers that are going to still exist over time? Right. I think if you think about what's happening to coding, it reminds me a lot of civil engineering. And so it's kind of a funky example. But, you know, civil engineers used to work pen and paper doing calculus. Will this bridge hold up or not? That's been obsolete for a very long time. All those calculations are done by a computer. They're kind of clicking and moving and they go on site, they collect some data, they talk to stakeholders and they're effectively project managing this computer that can do the physics part of their job. For them. It's important that they understand the physics in case something looks strange. But they're not doing much physics. Right. That's clearly where software engineering is heading in the near term. In a lot of companies, it's already there. And these companies are still hiring software engineers because that job is valuable. And in fact, each individual software engineer is way more productive than they were before. And there's happily elastic demand for software. Like we still are undersupplied with software in the world. And so there's quite a bit of room to go to add those. And so I'm not necessarily bearish on the demand for software engineers, at least for the next three to five years. Beyond that, if things get weird, hard to tell. But then for people more broadly, I think the best advice is just adaptability. You know, constantly trying and testing these tools, making sure you're staying at the cutting edge of them and then being aware of what's human. Right. I think in like in my work in venture investing, you know, there's a lot of data that comes out of human relationships that an AI wouldn't have access to. You know, an AI can't call its friend at another fund and ask how a company's doing. Not yet, at least. You have to make some friends first. Right.

B (40:49)

They're talking about, they are talking to each other on moltbook. Right.

D (40:51)

They're talking to their Molt Book. Yeah. So maybe if there's an AI agent from Sequoia and an AI agent from Andreessa.

B (40:55)

I was intrigued by that for about five minutes.

D (40:57)

Yeah, it's pretty fake. It was very evocative, but pretty fake.

C (41:00)

Well, also there was that Wired article of the guy who, like, infiltrated as a bot and pretended to be a bot. It was pretty awkward.

B (41:07)

They're like, oh, why are we. Let's create a new language just for us. They're not making new languages.

D (41:11)

Right. But yeah, I think. I think the rough mental model is if there was any effort to outsource your job to India. Yeah, that's risk, because that tells you that job can be done by someone who's not physically present in a space. And, you know, if you like working on problems in isolation, not socially with other people, you know, grinding out math problems or little coding assignments, that's probably also a pretty tough place to be. Yeah, it's going to be a more social world.

C (41:36)

This is something we've touched on before, which makes me kind of sad, which is the edge in the AI world becomes like, sociability. Right. And to some extent, we talked about this in the context of look smack. I know you love it, Joe. I do not.

B (41:52)

Can I take two things, two little observations from my time vibe coding in 2026 that are interesting. One is I have zero technical background. I've been surprised by the speed with which I can build intuitions about when it's going off the rails, like when it's doing something that doesn't seem right. Like, I joke that Vibe coding is just typing, make it better, press Enter over and over again, and then hitting yes when it offers to do something. You actually can start to build an intuition fairly quickly when this doesn't make sense. And then the other thing, and this relates to your question of trusting the AI. So one of the things I'm doing is I'm having a lot of documents get annotated, and I do that through the Claude API, which actually runs up the bills a little bit. And one thing, this API run was going to cost like a hundred dollars. And I stupidly asked Claude, I was like, is this a good thing? It's like, well, when you're done with this API API run, you're going to have this annotated asset that no one else has done, and that'll be very. It was sort of useless, what I did, so you shouldn't.

C (42:53)

It's selling itself.

B (42:55)

It's like, oh, yeah, use the API, Joe, run this, like, annotate all these documents. It wasn't actually like a good use of my time. So you can't really always. They're just gonna, they're gonna just sell these things. So Tracy asked about data and stuff. There's one other sector that I'm interested in. You see these companies like Moody's or Fair Isaac or S P Global that have an index.

D (43:17)

Yeah.

B (43:17)

And they're getting, they're selling off too. And it's not like this is another area where like it's, you know, people are fund managers for a long time. Unless things get really weird, the super are going to be like benchmarking themselves off of like the S&P 500 for a long time or lenders are going to be using the FICO for a very long time, etc. Intuitively would strike me as this would be a very hard thing for AI to replace.

D (43:41)

I share your intuition. Right. I can't say I fully understand the sell off in these companies. I wonder if there's not some parts of their businesses that are more services or consulting heavy that people are. There's often like combinations. Right. Where like I don't think anyone's suspecting that like, you know, The S&P 500 is going to be displaced as an index 500 because of. Yeah, maybe. But look, we're in a world where folks are very happy to shoot first and ask questions later once AI risk comes out.

B (44:09)

Actually, going back to your hedge fund time, how much is it? Just the sort of the nature of hedge fund traders right now where there's very little stomach to take any downside risk and appear to look stupid for missing holding the bag here. And how much do you think that's contributing to some of these market moves?

D (44:27)

It's a great question. I won't speak to my fund because I think CO2 and tiger cubs like it are a fairly small share of the overall market in dollars. But if you look at trading volumes, the POD shops, Citadel, Bally, Asney, Millennium are very large share of the volumes. And yeah, those people can't afford drawdowns. Right. And the scary thing about this for them is because it's not fundamental because the companies aren't struggling themselves. They have no idea when it will stop. Right. And so you're left predicting this thing and you're like, well, I bet my career that people are going to feel better about software companies in three to six months than they do right now. And you know, you're one OpenAI model release or anthropic model release away from More fear. And so I do think there's a lot of short termism right now and there's all. But again, I think we'll come back to the SBC point. But there's also no valuation support, no real valuation support. You know, in normal times if companies were like this, they'd be buying back a bunch of their stock, shrinking their share count, issuing dividends. You know, I have a friend who works at a mutual fund where there's a lot of dividend funds that would love to buy dividending companies growing 10 to 15% like a lot of software companies, but they're not right. And so you've kind of lost the ability to kind of put an actual floor in underneath the valuations as a result of that.

B (45:42)

Jared Sleeper, thank you so much for coming on Outlaws and explaining how software works. My pleasure.

D (45:47)

Super fun. Thanks.

B (46:01)

Tracy. I thought that was really interesting. I'm very fascinated with this idea that in the short term most of these businesses are doing fine. In the long term they might go to zero, but also in the short term they're not really doing fine because actually on a GAAP basis they're not making much money. I guess it sort of makes sense that they're all selling off right now.

C (46:21)

Yeah, I keep thinking this is probably a stretch, but I keep thinking back to that book Bull Jobs, remember? And like the argument there is a lot of jobs exist not because like people are doing anything specific, but because they're like providing some sort of social value in a way. So for instance, like you have a person who is essentially the designated scapegoat for senior management. And I keep thinking of, you know, business is basically an ecosystem of different players. So it might be that in the new AI world, the role of software companies just kind of changes. Like their social role changes. And I don't know what the price or the valuation looks like on that.

B (47:05)

It still feels like to me, what do I know? I have no, I was going to say something about how businesses were going to buy. What do I know? I have no idea how businesses are going to buy software in the future. I did think that was really helpful. Like I really don't know anything about how the software business works generally. So I find that very helpful. One other interesting thing though, and it may sort of speak to thinking about this risk, is just the idea that like even high end software is not that much money, right? So if you have a salesperson who's making $250,000, what is $1,000 a year from Salesforce to do their job particularly. And then also given the fact that, you know, free and open source software has existed for a long time, but still, you know, you want to pay an implementer for a company that like manages, etc. Getting from like here to there, where okay, AI really changes the nature of software buying. Does feel like you have to get into this is going to be weird towards here, but maybe things are. I think things probably are going to get really weird.

C (48:03)

Yeah, I think that's, that's a pretty good bet, right? Like if you bet on weirdness, if there is a weirdness index, someone should.

B (48:10)

Build that weirdness index.

C (48:11)

That would be a pretty good investment.

D (48:13)

Yeah.

C (48:13)

All right, shall we leave it there?

B (48:15)

Let's leave it there.

C (48:16)

This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

B (48:21)

And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest Jared Sleeper. He's at Jared Sleeper. Follow our producers Carmen Rodriguez at Carmen Armand, dashiell Bennett at Dashbot and Kale Brooks at Kale Brooks. For more Oddlaws content, go to Bloomberg.com OddLaw Trust, the daily newsletter on all of our episodes and you can chat about all these topics 24. 7 in our Discord Discord GG oddlots.

C (48:45)

And if you enjoy Odd Lots, if you like it when we talk about the Cassastrophe Cassastrophe then please leave a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the page Bloomberg Channel on Apple Podcast and follow the instructions there. Thanks for listening.

B (49:28)

Sam.