Benchmark’s Chetan Puttagunta on the Past, Present, & Future of Software

Summary

---

The Peel with Turner Novak

Benchmark’s Chetan Puttagunta on the Past, Present, & Future of Software

Date: March 4, 2026
Guest: Chetan Puttagunta (Partner, Benchmark)
Host: Turner Novak

---

Overview

In this engaging episode, Turner Novak sits down with Benchmark partner Chetan Puttagunta for an in-depth conversation about the evolution of software—from mainframes to SaaS to the current explosion of AI-native applications. Chetan shares granular insights into Benchmark’s investment approach, tells the inside story of runaway success Manus, and explains why the next phase of software will disrupt both startups and established giants like Salesforce. Key themes include the new economics of AI apps, venture dynamics, and how infrastructure and application layers are rapidly evolving.

---

Key Discussion Points and Insights

The Manus Case Study: From Beta to $100M ARR in 8 Months

• Discovery & Diligence

  • Chetan recaps how he discovered Manus’ demo video within hours of its March 2025 YouTube launch ([01:00]):

    “I saw it in the first couple of hours...and I was thoroughly wowed by the experience. Manus was presenting an agent product that could actually get further on tasks than any other AI product had at that point. It really felt magical when I first tried it.”

  • Unique to Manus: Not just multi-model use, but decomposing tasks into thousands of subtasks and solving each in parallel ([02:59]):

    “They’d taken the idea of breaking a task into subcomponents and using lots of models to solve them to such an extreme degree, I don’t think anybody had tried that yet.”

• Growth and Acquisition

  • Product’s closed beta went viral in Japan, leading founders to run user meetups in Tokyo ([04:10]).
  • Manus launched public GA April 2025.
  • Achieved $100M ARR in just eight months; with consumption revenue, $125M run rate—“the fastest company to have ever gone 0 to 100 million” ([06:13]).
  • Meta acquired Manus, “acquiring a team deeply knowledgeable about how these APIs work and how to get further on a task” ([08:44]).

• Core Use Cases

  • Identified three primary consumer demand areas:
    1. Deep research (longform writing)
    2. Coding assistance (especially for nontechnical users)
    3. Slides/presentation creation
  • Manus relied exclusively on Anthropic, OpenAI, and Gemini APIs, blending them for depth and performance ([08:10]).

---

Investing Amid Twitter Heat and Geopolitical Scrutiny

• Pushback on Social Media
  • Chetan explains that despite public criticisms (including over Manus’ founders’ Chinese heritage and Singapore HQ), Benchmark focused on fundamentals—team quality, technology, and global product fit:

    “For us, we want to invest in great people. The product wasn’t available in China. It was a worldwide business. Those are the kind of businesses you have to back as a generalist investor, especially as somebody looking for consumer AI.” ([11:13])

  • Discusses the global distribution of AI talent:

    “Jensen Huang of Nvidia recently said something, half of the world’s AI scientists are Chinese and half are American.” ([12:49])

---

Social Media Strategy: Peaks, Pauses, and Community Value

• Chetan recounts how summarizing earnings calls on Twitter unexpectedly built a large audience of software enthusiasts during 2018–2020 ([17:52]).
• After returning to in-person meetings post-pandemic, tweeting declined; he notes real-time commentary creates more value than delayed posts ([21:47]).
• Recent engagement uptick driven by “the fundamental shift at the application layer with AI” and high-impact launch videos ([22:00]).

---

Software History: From Mainframes to AI-Native Applications

• Era Progression ([23:09]–[31:04]):

  1. Mainframes: First application automation
  2. Client-server: More custom apps
  3. Internet: Huge explosion in app volume (two orders of magnitude, especially for consumer apps)
  4. Cloud: Centralized hosting (EC2/S3, ~2009-2011), removes capital barriers for developers; coincided with mobile App Store (“a capital-intensive process replaced by cloud and credit card”)
  5. SaaS: Incumbents (Salesforce, Workday, ServiceNow) expand into adjacent categories and make distribution difficult for new entrants ([31:24–33:04])
  6. AI layer: New Cambrian explosion—every horizontal category “up for grabs” again
     > “The SaaS comparable looks really bad. When you open an [AI app], the experience is that stark.” ([41:46])
     > “Do not complain about negative gross margin software company...if you’re seeing the patterns you saw in the last phase.” ([55:26])

• Key Insight:

  • Each technological wave slashes development cost and increases distribution reach. AI is seen as the next such tectonic moment ([34:30], [36:43], [47:18]).

---

AI Applications vs. SaaS: The Next Competitive Frontier

• Why Incumbents Will Struggle

  • On Innovator’s Dilemma ([43:18]):

    “For a SaaS company to beat a native AI application...they would have to break their fundamental architecture, rebuild, redo the business model, and retrain distribution.”

  • Example: Siebel “on demand” vs. Salesforce—a detailed lesson in why incumbents can’t move fast enough ([44:29]).

• Advice for SaaS Giants

  • Acquisitions as defense:

    “If you’re a SaaS company right now, you should really think about spending 10–25% of your market cap to buy AI applications.” ([51:29])

  • History shows incumbents always see deals as “too expensive”—until it’s too late ([57:55]):

    “These AI application companies were much cheaper to acquire in 2023...it’s happening right in front of us.”

• Do founders even need to sell?

  • The late-stage capital environment makes it easier for AI startups to raise and go for long-term independence ([64:28]).

---

Public Market Dynamics: The Coming AI IPO Wave

• Shifts in investor demand:

  “For the first time...I’m hearing bankers say things like, ‘Yeah, $100 million ARR, we could probably take that public.’ Haven’t heard that in a while.” ([66:48])

• Capital Flows:

  • "If you look at net new ARR, where is it going? It's really just going to all these AI companies." ([68:32])
  • “OpenAI and Anthropic added as much revenue as every single publicly traded software company [since ChatGPT’s launch].” ([69:21])

---

Benchmark’s Investment Playbook

• Approach to Valuation and Structure

  • Board-centric; ownership is flexible as long as partnership with founders is strong ([70:17], [74:14]).
  • "The valuation, frankly, is not the governor in our investment decisions. The discussion really isn't about the valuation, or the deal structure." ([71:13])
  • Partnership model: Four equal GPs, eight to ten investments a year, highly collaborative ([70:35], [72:39]).

• Historical “Nontraditional” Rounds

  • Benchmark’s flexibility: From Nordstrom.com spinout to a “growth” round at Twitter, and continuous willingness to do “atypical” deals if the founder is compelling ([74:29]–[76:54]).

• What They Look for in Founders

  • Deep domain or technical insight, often at the pre-product stage:

    “Typically investing seed Series A—no product, no revenue, no metrics. But some deep insight that the founder has.” ([82:49])

---

AI Code Generation and Infrastructure

• “There’s massive demand for codegen products—across consumer, B2B, and prosumer.” ([85:25])
• Margin debates are premature; chip and cloud innovation (e.g., Cerebras) will significantly reduce inference costs, changing future P&Ls ([86:23]).

---

Notable Quotes & Memorable Moments

• On AI App Unlock:

  "Meta is acquiring a team that's very deeply knowledgeable about how these APIs work and how to get further on a task, depending on the kind of task, with a certain set of APIs." – Chetan ([08:44])

• On Acquisition Opportunity:

  “Companies are about to get gigantic. Manus went 0 to 100 in 8 months. Once they're at $100 million, they continue to scale. These companies are really big, really fast.” – Chetan ([52:33])

• On the Investor’s Mindset:

  “If a company doesn’t work, it’s a 1x error. If a company works, it can generate a lot of returns. So you just want to be in companies that work, and so you don’t worry about the downside.” – Chetan ([84:57])

• History Repeats:

  “It’s really interesting that the SaaS companies have forgotten their own state...They are now the incumbent and not embracing what it takes to buy the upstart.” ([61:00])

• Foundational Philosophy:

  "We want to be a part of an organization where you could do deals like Manus, Sierra, Lagora, Fireworks, and LangChain—all in a span of 12 months..." ([77:06])

---

Timestamps for Key Segments

• Manus origin and hype — [00:26]–[06:23]
• Manus' use cases & technical edge — [06:23]–[08:44]
• Twitter controversy & global AI talent — [11:08]–[14:23]
• Social media engagement, public market pulse — [15:39]–[22:00]; [66:48]
• Era-by-era evolution of software — [23:03]–[33:04]
• AI vs. SaaS, incumbent challenges — [41:46]–[52:33]
• Acquisition logic, public vs. private market multiples — [57:55]–[62:18]
• Benchmark's deal approach/strategy — [70:17]–[77:06]
• What Benchmark looks for in founders — [82:49]–[84:57]
• AI codegen, margin, infrastructure outlook — [85:25]–[88:03]

---

Tone and Style

The conversation is candid, technical, and occasionally irreverent—with subtle humor in the banter, e.g. comments on Twitter activity, “directionally correct” IRR stats, and banana-breaking demonstrations at the end. Both Turner and Chetan keep industry lessons accessible by weaving in metaphors, founder-centric language, and concrete case studies.

---

Transcript

[Jason]

Well, cool. Chathan, welcome to the show.

[Chathan]

Thanks for having me.

[Jason]

I want to jump right in first thing on the docket. So you're coming off you invest in a company called Manus.

[Chathan]

Yes.

[Jason]

And I saw your partner Eric, who I had on the podcast this summer. He tweeted something about. It was like a thousand percent irr. I forget exactly what he said, but it was like, oh, that's a pretty big number. What's kind of the story behind Manuscript?

[Chathan]

Absolutely. And you had a great episode with Eric, too. Manus was just one of the unique consumer AI agent products that really spiked, and obviously the Meta acquisition has been announced. And it was an incredible journey to be on that product journey with that team. They're just the six founders, which is a large group of founders, but I think that seems to be a theme in AI, which is to have a lot of founders.

[Jason]

The more founders, the better. So if I meet someone, 10 co

[Chathan]

founders, it's instant check. That's right. Are just some of the most resilient, brilliant, and kind people that you'll meet. And the story really starts with, these dates are directionally accurate, if not precise. But first week of March, they posted a YouTube video with a demo of Manus. And I saw it in the first couple of hours of them posting it. Somebody I follow on Twitter posted it on Twitter, saying, I saw this cool demo of an AI agent. I clicked into the YouTube link, saw the video, and then went to the website and signed up for the beta. And I think a couple hours later, I got beta access to Manus, and I used it, and I was thoroughly wowed by the experience and the reason I was thoroughly wowed. So this is March of 2025. Obviously we're well past the ChatGPT moment. You know, I was a user of ChatGPT, user of Claude, user of Gemini, user of Cursor, et cetera, et cetera. But what they were presenting was an agent product that could actually get further on TAS than any other AI product had at that point. And it really felt magical when I first tried it. And immediately I texted all my partners and I said, sign up for the beta of this thing. It's really magical. And then I just reached out to the founders because I wanted to know who they were and just wanted to know about what they were working on and how Manus worked. How did Manus get so much further on TAS than a regular AI chatbot? What were they doing? What was their key breakthrough? What did they learn to get there?

[Jason]

Yeah.

[Chathan]

What was it and so what they had figured out was, I think at that point, people at the application layer had learned that you could use multiple models at once to do more with a task. What I don't think people had quite tried was breaking up a task into a thousand little tasks, and then for each subtask, using multiple models in parallel, trying to get past that sub step. And so they had just taken the idea of breaking a task into sub components and using lots of models to solve sub components to such an extreme degree, I don't think anybody had tried that yet. So we reached out and they got on a zoom with us on a Monday and explained to us what they were doing. And it was super compelling. And at the time, for some reason, the beta had gone really viral in Japan. And so the team was in Japan trying to figure out why it was going viral in Japan.

[Jason]

So you had to go to Japan to figure that out? Apparently, yeah.

[Chathan]

So they wanted to just talk to users because they wanted to be like, why is this breaking out in Japan? Interestingly, I don't know if you know this, but like, when ChatGPT first launched Reddit, Japan actually was one of the places it went viral early. And so there was something about consumer chatbots and consumer AI and the Japanese market that seems to be an early signal of things that can go viral in consumer AI. And so the team was in Japan talking to users, running a bunch of user meetups because they wanted to know why it was going so viral in Japan. And so we did a zoom on a Monday and I wanted to meet them in person. And so they were like, well, we're in Tokyo, so you're welcome to come hang out. And so I think that Friday

[Jason]

I

[Chathan]

flew out, and before that a couple of the founders were in San Jose at the time. And so I met them, you know, in San Jose. And then on Friday I got on a flight. Friday night, I think I got on a flight to go to Tokyo. And then Saturday night, Pacific time, they presented to the partnership. And then over Zoom, over zoom. And I was there in person with them in Tokyo. And then after their presentation, Red, who was one of the founders and CEO Red and I went for pizza and beer and Gato handshake. And then that was like the start of the relationship. They ended up launching the product in general availability first week of April. And so they basically ran a one month beta with sort of like a closed beta where you had to sign up and then they would let you in. And then the product exploded. And in December, they announced that they had gone 0 to 100 million ARR in eight months and 100 million ARR. And then if you counted the consumption revenue they were generating, it was like 125 million run rates. And I think that's the fastest company to have ever gone 0 to 100. I mean 8 months is just an outstanding speed record to go 0 to 100 million.

[Jason]

Yeah, it's pretty good.

[Chathan]

The interesting thing about this product was where it was being used and how it was being used. And so the three primary use cases that emerged were deep research coding, which was like a fascinating use case.

[Jason]

Interesting.

[Chathan]

And three was slides. And so the three primary things that consumers were using Manus for was those three things. So if you dig into each of those components, Manus was getting further on deep research and writing further more detailed reports than other AI chatbots on coding. It was interesting that Manus was being used by non technical people to code websites, applications, prototypes, mobile apps, whatever. And they were basically using it as a technical companion and largely by people that were not technical. And something about the user experience, something about the ui, something about how far Manus would get on a prompt with a website or developing a mobile app really attracted a lot of like consumers, prosumers. And then finally the third one was slides. And that made. That of course makes a lot of sense. If you're really good at deep research and people are doing a lot of deep research, they want to turn the deep research into a bunch of slides that they could use for work or whatever. And so those were the three primary use cases that emerged. They just kept building features based on consumer pull. And then obviously it caught the eye of Meta and acquired the company. And couldn't be happier for the founders. Like they're incredible group, incredible technologists. I think that they had built a product that really blended multiple AI models. The three AI models, they exclusively used Anthropic, OpenAI and Gemini models. And they had just created a way to blend the APIs of these three models to just get further on tasks. And I think it's just from my perspective, Meta is acquiring a team that's very deeply knowledgeable about how these APIs work and how to get further on a task, depending on the kind of task, with a certain set of APIs. And I think if you just project out the consumer market the next couple years, I think you're going to see more and more consumers and prosumers want to just get things done. And this Manus team certainly has figured out a way to do that.

[Podcast Host / Narrator]

This episode is brought to you by Numerl. Numeral is the fastest, easiest way to stay compliant with US Sales tax and global vat. It's easy to set up and they automatically handle all registrations, ongoing filings, and their API provides sales tax rates wherever you need them with all the integrations you need. Numero sports over 2,000 customers in both

[Jason]

the US and globally and they pride

[Podcast Host / Narrator]

themselves on white glove High touch customer service. Plus they guarantee their work and they'll cover the difference if they mess anything up. They're fresh off a Fundraise closing a $35 million Series B from Mayfield, which they're going to reinvest into building an even better product. If you want to put your sales

[Jason]

tax on autopilot, check out Numero at

[Podcast Host / Narrator]

their new domain numeral.com that's n u m e r a l.com for the end to end platform for sales tax and VAT compliance. This episode is brought to you by Flex. It's the AI native private bank for business owners. I use Flex personally and I love it because they use AI to underwrite the cash flow of your business, giving you a real credit line. The best part is 60 days afloat, double the industry standard. Flex has all the features you'd expect from a modern financial platform like unlimited cards, expense management, bill pay that syncs with your credit line and their new consumer card. Flexelite Flex Elite is a brand new ramp like experience for your personal life. A credit card with points, premium perks, concierge services, personal banking, cars and expense management for your family. Net worth tracking across public and private assets and and a whole lot more fully integrated with your business. Spend one card for your businesses, one card for your personal life, one card for everything. To skip the waitlist, head to Flex 1 and use My Code Turner to get an additional 100,000 points worth $1,000 after spending your first $10,000 with Flex Leap, that's Flex 1 and Code Turner for $1,000 on your first $10,000 of spending. Thank you Flex. And now let's jump in.

[Jason]

So you got some heat on Twitter when you invested what was going on there?

[Chathan]

People are welcome to say whatever they want on Twitter and we had a great deal of conviction that this company was a great company founded by a great set of entrepreneurs and ultimately the company at acquisition was about 105 people, roughly maybe a little bit bigger. And the team was 95 people in Singapore, a couple people in Tokyo and a couple people in the Bay Area. And the company was like Pure technologists, pure product engineering people building exclusively on American models like anthropic, Gemini, OpenAI models. They weren't fine tuning or post training or building any of their own models. They were using APIs from American models and their product was hosted fully on American clouds. So they were using largely Google Cloud, AWS and Azure to host the product. And so if you just looked at what they were doing, they were delivering incredible consumer value for a great price and had built an incredible business. The founders happened to be of Chinese origin. And for a company headquartered in Singapore and for us, we want to invest in great people. And I think that if you just looked at, for example, this has been published on Twitter or X a lot, which is rosters of great AI scientists and AI research scientists.

[Jason]

They're all Canadian.

[Chathan]

A lot of them are Canadian, a lot of them are American and a lot of them are of Chinese origin. And Jensen Huang of Nvidia recently said something, half of the world's AI scientists are Chinese and half are American. I think there's a large population of AI researchers, AI engineers and AI product people that are of Chinese origin. And I think that to me, I want to back the smartest people building great consumer products. And this was a great consumer product targeted for the world market. The primary users of this company were in the us, Japan, Europe, Brazil, India, et cetera. The product wasn't available in China. Manus couldn't be accessed in China. They didn't have a business in China. It was like a worldwide business. And to me, those are the kind of businesses you have to back as a generalist investor, especially as somebody that was looking for consumer AI. For me, going into 2025, it was pretty clear to me that somebody was going to build a consumer AI agent or a consumer AI product that was going to allow people to get tasks done. I mean, this was pretty much the thing that everybody was talking about. If you remember towards the end of 2024, they were saying 2025 was going to be the year of the agent.

[Jason]

Yeah, it was kind of starting to piss me off a little bit. People couldn't shut up about it.

[Chathan]

And so if you just were paying attention to what Everybody involved in AI was saying was that 2025 was going to be the year that we were going to start to see agent products really come alive. As soon as I saw the manuscript demo on YouTube, it was pretty clear to me this was the instantiation of exactly what people were talking about. And you just had to due diligence on the company and the founders to realize this was a great company, a great product, and was going to really do a lot for consumers and provide a lot of value. And so we had all the confidence that this was a great company and this was a great, great set of founders. And if people want to say a bunch of stuff on Twitter like that's their problem, not mine.

[Jason]

I feel like you've gone through these waves of like being active on Twitter or I feel like when I first met you was maybe one of your earlier waves didn't. Because I actually have. I realized I had notifications turned on for your tweets. Oh, and you still have them? I do, yeah. I don't know. Sometimes I like, like or reply yes, yes. But there's definitely a period where you didn't. That's right. So how do you think about your being more active on social or pulling back a little bit?

[Chathan]

So I sort of ramped up my activity on Twitter a lot starting in 2018, 2019, primarily because at that time there were a lot of companies in software going public, a lot of public activity around software. And then we entered the pandemic where I was in front of a computer basically all day like most people. So I was on Twitter and X a lot. And then software, as you know, at that time experienced an extraordinary moment in time of exponential growth. And in that moment you realized that software evaluations that were in the public markets and private markets really did feel unsustainable, but you kind of had no choice but to accept the unsustainable nature of those valuations because you had to play the game on the field. And I think that other venture capitalists, including Bill, have talked about this, which is that in venture you only have one strategy, which is like you can only go long. In our asset class, there's no such thing as shorting something. That's just not a thing that we.

[Jason]

It's just like selling maybe own the shares like you sell, maybe.

[Chathan]

But even that if you're an early stage investor buying 15, 20, 25% of a company. Right. You're just not a highly liquid market for that. And so you're going long only. And so you're reacting to the market constantly. And so in that moment, it just really felt like we were in a really wild time. And the kind of growth that software companies were experiencing, the kind of like earnings acceleration software companies were experiencing at that moment in time, the amount of spend that was going to clouds, cloud infrastructure, it was just a really fascinating time. And I decided to just talk about that. Because it was something that was super fascinating to me. And I just started at that time, just starting to listen to every earnings

[Jason]

call, as one does in their free time.

[Chathan]

Yeah, that's what I was doing. I was just summarizing and highlighting the things that I was learning off these earnings calls. And for some reason, it found a large niche audience on Twitter. And, yeah, I mean, it ended up building a pretty sizable audience, which was, frankly, surprising. I was quite surprised that that many people were that into software companies and software.

[Jason]

It's such, like, a simple thing, too. And it's like something where you arguably would have done that anyways, and maybe you would have also summarized it. Just instead of sending it to a friend or a couple coworkers, you just put on the Internet.

[Chathan]

That's right. And.

[Jason]

And there's a big audience for that.

[Chathan]

That's right.

[Jason]

People want to get smart. People want to learn things.

[Chathan]

So that happened for a bit, and then I think May of 2021. So these are, again, I think this is when we reopened our office, the benchmark office in San Francisco. And that's when you can clearly see my Twitter activity start to fall off. And so once we reopened our office in San Francisco, the number of people that wanted to just meet in person went through the roof. I think there was just. We had basically been meeting everybody on Zoom for over a year, and I think there was a moment where, as people started to move back into San Francisco, the early stage entrepreneurs just wanted to do everything in person. And so we saw probably around the summer of 2021, a lot of activity moved to in person, and a lot of people just wanting to meet in person and, you know, reconnect in person, much like we're doing now. And I think that's, like, when we may have also met for the first time around that time, or maybe we met in 2019.

[Jason]

I think we met before COVID Okay.

[Chathan]

I'm trying to remember. And then we met again, because I think you made a trip out after.

[Jason]

Yeah, there was one. We got breakfast. Was it like at the Rosewood.

[Chathan]

Yeah. In Menlo Park. That's right.

[Jason]

Maybe the second time we.

[Chathan]

That's right.

[Jason]

Maybe that was the first time, actually, that we met in person. I can't remember.

[Chathan]

I cannot. Yeah, because we met a bunch of times on the Zoom while we were in the pandemic. Anyway, so I think, like, as a result of, like, my schedule just going back to mostly in person, I stopped Interact. I just stopped tweeting, and I just thoroughly enjoyed meeting people in person. Far more than listening to earnings calls and stuff. I just stopped listening to earnings calls. And then if you stop listening to earnings calls, there's not a lot to summarize. And then I just stopped paying attention to public software companies as a result. And you always still pay attention to public software companies in your own personal time and to understand the industry and stuff like that. But I wasn't paying as close of attention as other people were, and I wasn't sort of like, looking at it first and offering first impressions. So, like, you know, it started to go back into the senior practice of, like, I'd read earnings releases maybe like, two weeks after they came out, or I'd read, like, analyst report two weeks after it came out. And whereas in the pandemic, I would read it like, the day of, it

[Jason]

was like your entertainment, like, your event was like, oh, man.

[Chathan]

That's right, that's right, that's right. That's what I was doing. And so once we came back into the office, it was just delayed again. And then I think, like, if you didn't. If you didn't tweet about it right at the moment, I think there was like a moment in time interest thing too, which I thought was interesting that revealed itself, which is like. I tried a couple of tweets that was like, on earnings that were like a month old or two months old, and it was basically like, people were like, I already knew this.

[Jason]

Yeah. Usually need a chart. Like, you need, like, a good chart that showed, like, how.

[Chathan]

Yeah.

[Jason]

AI adoption maybe was like, changing in Salesforce or something. But you run the risk of, like, the day of earning. Somebody probably grabbed that in.

[Chathan]

That's right.

[Jason]

Tweet it.

[Chathan]

That's right. And so, like, there was a timely nature to it. So I didn't. I didn't do that. So I stopped doing that. And then I think I'm back more on Twitter now with all this, like, AI stuff happening. And I think the. The reason that I've started to re engage is watching launch videos. Yeah, sure. Launch videos are really impressive now. They are very good, very high investment. I think the thing that I'm starting to tweet more about is just that the stuff that's happening at the application layer with AI is a fundamental shift. And I think that it is a fundamental shift on the same scale of on prem to cloud and unfirm to cloud took a long time.

[Jason]

Can you maybe give us, like. So Everett on your team was like, oh, you gotta ask Jason, like, give us a history lesson on eras of Software.

[Chathan]

Absolutely.

[Jason]

Can you maybe walk us through as early as you can recite and talk us through all the way to today?

[Chathan]

Go all the way back to the abacus. Start there.

[Jason]

So King Nebuchadnezzar, right?

[Chathan]

That's right. So we came up with the number system. No. And then look, I think it started with mainframes. Like, mainframes is when we started to get real application software level stuff.

[Jason]

And so this was like massive machine that weighed 50,000 pounds.

[Chathan]

That's right. And like you would create. That's when people started to create custom applications to automate stuff. And whether it was for the defense sector, whether it was for the public sector, for private enterprise, that's when you started to automate things using mainframes that then transitioned to client server. And then you had the Internet show up. And then the Internet obviously changed everything with regard to application software. So all of a sudden you had consumer applications and then you had B2B applications. Internet also helped centralize servers and how, like clients were served. It created edge networks.

[Jason]

Why is that all important? How did that change? Kind of the business model, it allows.

[Chathan]

There's two. So every new wave, two things happen. The number of applications created exploded each time.

[Jason]

Every time.

[Chathan]

Every time. Like the number of applications created to serve either businesses or enterprises exploded each

[Jason]

time is this like it increases like an order of magnitude, like 10x. Is it?

[Chathan]

Yeah, yeah.

[Jason]

Okay.

[Chathan]

But the Internet was like a hundred X, so it was like two orders in magnitude. So so each time there's a big jump in the number of applications. And then there's also a big jump in the number of companies creating applications for either consumers or businesses. And so these are enabling technologies. And then you had Internet, Internet showed up. And then you went from client server and then you went to basically the cloud model, which was you centralized where all the servers are hosted, and then you serve people via the Internet, the browser. And that ended up being sort of the genesis of cloud. And then the cloud thing ended up being extraordinarily transformational.

[Jason]

What was the biggest kind of transformation?

[Chathan]

I think number one of all was Amazon. And so Amazon, when they released EC2 and S3, which was like cloud computing and cloud storage in 09, 10 and 11 is when you started to really see that become very, very significant. And if you were in, you know, around the Bay Area around that time, what you noticed was a dramatic shift in Net new companies, they were all using Amazon. So if you were an app developer when the App Store first came out in 09, and your app Was like really working. You still had to go get server storage in downtown San Francisco.

[Jason]

Like you had to buy computers, you

[Chathan]

had to buy a rack. And then, you know, you could go to like Equinix and they'd sell you space and then you would have to rent servers from them. Or you'd have to go to like Dell or hp or like you could go to like Quanta, like if you really wanted commodity boxes and they would sell you these servers and if you bought your own, you'd have to like come put them in and then install them and wire them up. Or you could get like the person that ran the facility to like, you know, you could rent the servers from them or you could buy the servers from them. But you had like this is what it required. And this wasn't. The crazy thing is that not that long ago, this is 2009, 2010. Like, it's not just, it's just not that long ago.

[Jason]

Yeah, that's like relatively speaking. I mean, I guess I'm almost 35 now, so like I was graduating high school. Like, yeah, to me that's not that long ago.

[Chathan]

Not that long ago. And that was like really the cloud. And it just so happened that it coincided with the launch of the App Store, which was in 2009. And so you had this cloud thing happening and you had mobile devices about to explode worldwide. And so the total demand for applications from both consumers and businesses was about to go. Like it was going to have two factors that were going to increase orders of magnitude. So it was like 10x multiplied by 10x. So it was like you had cloud happening, which basically meant like more application developers could develop applications.

[Jason]

It was just easier to get off that, off the launch pad.

[Chathan]

That's right. You didn't need to think about servers, you didn't need to think about renting space. If you were running sort of like demo apps or prototypes, the standard way to have done it was to bought a server and plug it into your apartment and that's where you'd host it. And then if you were a solo entrepreneur, this is how you do it. And then you'd go ship it. Once you were ready to launch it, you'd go get some servers, plug it in and then hopefully it would work. This thing was just, it was one capital intensive because you had to go invest the capital on compute and storage.

[Jason]

You also had to figure out, how do I run this server thing?

[Chathan]

100%. And it was like, it was expensive and so the barrier to entry was high. And again, this Is the other thing about each wave of technology. Like, each wave, the barrier to entry for developing an application went down. And so with cloud and mobile, it was one, the cost of like deploying a prototype or an initial version of application went way down because you could go put it in the cloud. And then two, the cost of getting to an end user also went down because you had this amazing distribution mechanism through the App Store.

[Jason]

It's just like a kid in a class would be like, check out the Snapchat thing.

[Chathan]

100%.

[Jason]

All these kids just downloading it and using it.

[Chathan]

That's right. And then you could pay Amazon on a credit card and it would be consumption based.

[Jason]

So you technically didn't even actually really pay upfront. Like you could.

[Chathan]

That's right.

[Jason]

You could not pay your credit card for 30 days or if you didn't,

[Chathan]

you got a loan. Yeah, you got a 30 day loan. Essentially. Like it was like. And so it was a remarkable unlock. And you know, you saw an absolute explosion in app layer. Like obviously mobile apps, like anything from like Instagram, Twitter, Snap, like that all was unlocked there. Uber, Airbnb.

[Jason]

You're just listing off benchmark portfolio, correct?

[Chathan]

That's right.

[Podcast Host / Narrator]

Yeah.

[Chathan]

Airbnb wasn't.

[Jason]

But did you guys have like a. Did you guys have a bet in the space?

[Chathan]

I'm trying to think of like rentals,

[Jason]

home travel, type of.

[Chathan]

This was before my time, so I don't remember.

[Jason]

You're not an expert on the portfolio?

[Chathan]

No, no, I should be, but I'm not. Unfortunately.

[Jason]

Bill's going to be listening to this. Like, come on.

[Chathan]

We may have had something, but I think they did great. I think the, you know, the 2011 fund is legendary for having just gotten mobile and cloud, like perfectly right.

[Jason]

Even WeWork. WeWork wasn't even mobile or cloud. How did that even get in there?

[Chathan]

You know, like, that was Bruce and so, you know you're gonna have to go to him for the history on that one. I don't know.

[Jason]

Yeah. What was that fund like?

[Chathan]

What's the performance? Benchmarks 7. And we don't publicly talk about our performance. You can find it on the Internet. There's like plenty of.

[Jason]

Okay, what's the number on the Internet? What does the Internet say?

[Chathan]

That's a good question. I don't know. I think the last thing it said is, yeah, you should Google it.

[Jason]

I'm googling it right now.

[Chathan]

Benchmark seven. I'll tell you if it's like high or low.

[Jason]

Said it was around $550 million fund in 2011 MM says it was roughly 25x before fees.

[Chathan]

There you go. Sounds pretty good.

[Jason]

Directionally correct.

[Chathan]

Correct. Directionally correct.

[Jason]

Good.

[Chathan]

Fun.

[Jason]

And then you added in your manus thousand percent ir. Yeah, pretty good.

[Chathan]

So that really unlocked the number of applications, ease of deployment, lowered the capital required. So that was, you know, mobile, that was cloud. We've been on that wave basically until call it 2018, 2019. I think what you started Covid was

[Jason]

like an incredible accelerant period also huge accelerant and almost like foot on the gas even.

[Chathan]

That's right. Huge accelerant through 2020. And then the other thing that was happening though was that the SaaS companies incumbents were starting to get really dominant, like the controlled distribution. And they would start to eat up not only their core category, but would start to eat up adjacent categories. And so this is how Salesforce went from being just CRM. They did CRM, service, cloud integrations, et cetera, et cetera. Like it became huge. Same thing with ServiceNow. They started with ITSM and then it expanded into like HR sales and all this kind of stuff. Like these SaaS companies started to get really, really big. And if you just looked at it from a startup perspective, it was actually harder to get distribution in 2020, 2021 because the incumbents had gotten so big and so powerful and basically penetrated every enterprise account. And if you were a brand new startup, you would show up and be like, well, I could do this Nichier thing 15 to 20% better. And then your buyer would be like, well, I could just go to the Salesforce thing and ask them for a discount and they'd probably just give me 5% off and that's probably easier. And so it was frictionful. Having a net new company to find its sort of niche and to be able to really go after a big horizontal category was really hard. And so what you ended up seeing 2020, 2021 was like a lot of hypervertical application software companies get created.

[Jason]

Give me an example for someone listening.

[Chathan]

There were a whole bunch of companies that were created for the construction vertical as an example. There were a number of companies created for the compliance vertical. Some of them are doing really well now, of course.

[Jason]

So it's basically like a what are things that Salesforce isn't doing or won't do?

[Chathan]

Salesforce, Workday, ServiceNow, what are they not doing? There's still opportunities. Then people would like go attack them. And some of them obviously became successful. But you didn't see the same Cambrian explosion of applications that you saw in 09, 10, 11, 12, it just wasn't this extremely fertile ground to create new startups, especially at the application layer.

[Jason]

It was mostly just a big kind of like got most of the value or absorbed most of the value.

[Chathan]

And obviously things dramatically shifted starting in 2022.

[Jason]

Yeah. So how do you kind of dissect what was going on in 2223?

[Chathan]

So, you know, GPT APIs were available in 2022 and this was the earliest signal we had gotten at Benchmark that people were thinking about developing new sets of applications. So we would meet entrepreneurs that would be playing with these APIs and saying like, hey, have you guys seen this thing? Like it generates like, you know, you can make this API call, you could do this and that, it's generating all this interesting stuff.

[Jason]

Maybe had like Jasper was maybe like a breakout at that time.

[Chathan]

There were a bunch of like people doing like copywriting as sort of the first use case because you could only interact with these things through APIs. And in 2022, November of 2022, obviously ChatGPT comes out. I think everybody that used the product that moment in time thought it was the most magical thing. And I think feeling like it was magical wasn't a particularly unique insight. I think everybody thought it was like, it was like, wow, this is incredible. I think what we as a firm did at that moment in time, which I look back on was, was particularly insightful, was to then say, this is obviously the way of the future. This is clearly what the experience of the future is going to look like, which is there is going to be some automated system at the back giving you the answer or finishing the task. And it became pretty obvious to us around the table that that was the way of the future. And so what we decided to do at that moment was focus heavily on AI applications. And I think this is one where, if you've just been investing in software like I had for, at that time, probably over a decade, this is the best thing that could happen to you as a software investor. Because now as an early stage software investor, you're a late stage software investor or like a private equity software investor, you now have a lot of portfolio companies that might be threatened. But if you're an early stage software investor, if you recognize the catalyst of a shift change and you think this is the next shift change. Yeah, next catalyst as big as a cloud. It's incredibly exciting to go all in on software applications again at that moment. So then it became pretty clear to us that every large horizontal category was up for grabs again. And it was a good time to go back into it.

[Jason]

So the Salesforce, the workday service, now

[Chathan]

all of this like all horizontal categories were up for grabs. And all we needed was entrepreneurs that saw the future by playing with ChatGPT and the OpenAI APIs. And then there were also net new categories to be created. You didn't only have to go after incumbents, you could like create brand new categories and sell software into areas that hadn't bought a lot of software before.

[Jason]

And that's because the software could solve new problems for you that it's been.

[Chathan]

That's right. And so this is where like coding assistance and legal software very specifically, like those are not categories of software that are pre established, like legal.

[Jason]

As you know, that was a dead zone. That was like not touch.

[Chathan]

It wasn't great. I mean I had, you know, to be fair, I had one successful company there, a company called Logical, which was an eDiscovery, which I invested in and had a very successful outcome. It was acquired by a PE firm of course, because like, you know, it's like Once you get eDiscovery customers, there's a lot of cash flow that shows up as a result.

[Jason]

What is eDiscovery? This is like the process of learning more about the case and like collecting evidence or something like that.

[Chathan]

Yeah. So basically whenever you have to go through like litigation, you know, you have to go discover a bunch of facts against that case that shows up in like emails and documents.

[Jason]

So this is like when someone says when, when we see those posts about like the Steve Jobs email, like an Apple, like it was through the discovery

[Chathan]

process of that, that was like an E discovery software found that, found that email. And so that those companies ended up being pretty interesting. There was a company in Chicago called Relativity that ended up becoming big. There were like some companies in Europe.

[Jason]

This is basically just like a SaaS document storage. It was like virtualized for search with search.

[Chathan]

Yeah. Okay. And so that was like the only category in, in legal software that worked. But you know, when AI shows up, you now can like start to meet entrepreneurs that are dreaming big again in horizontal categories. And that also allows you to invest in AI app enablement. And so it's like all of the frameworks, all of the like new cloud infrastructure to host all these AI things, these all open up as opportunities. And so if you look at our investments on 2022 onward in AI, there's one central theme to it all. It's like all AI applications and application enablement and that was very much on purpose because we just found that there was tons of opportunity there. In our view, it was also opportunity that a lot of people weren't paying attention to in our industry. Because I think people at that time saw the magic of ChatGPT rightfully. And then a lot of AI labs ended up getting funded at that moment in time.

[Jason]

In 2022, how many did you guys invest in a benchmark?

[Chathan]

We invested in one open source.

[Jason]

One that feels like a classic benchmark approach. It's the open source version of something.

[Chathan]

Oh yeah. I mean, we, you know, as you know from our history, we deeply believe in open source and in this case it would be open weights. And you know, there were a lot of open weights models, including LLAMA models. But then more recently, if you look at, you know, models that are coming out of China that are open weights, like these are, these are really unlocking a lot of additional models in the US that people are like borrowing techniques from these open weight models. They're borrowing these open models themselves and then customizing them, crafting them, fine tuning them, applying RL to it, all that kind of stuff. And so open weight models in general are a category that I fully believe in. And I hope that LLAMA continues meta with llama models, continue to keep them open. But I think that we spent a lot of time meeting with entrepreneurs building AI applications and entrepreneurs that wanted to enable the next generation of AI applications. And so if you look at that fund and you look at the investments that we made, we made a number of seed and series A bets at that time in 2022 in companies like Sierra, Lagora, Fireworks, Levelpath, LangChain that just are entrepreneurs building very horizontal applications that are attacking gigantic markets with truly innovative tech. And when you open this application and you experience it for the first time, the SaaS comparable looks really bad. Like it just looks like.

[Jason]

So give me an example of one of those contracts that I might come across in the wild.

[Chathan]

I think if you just ever interact with Sierra as an enterprise customer, like if you were to buy for this podcast, if you ever wanted a customer agent, you have two options. You could go to like a traditional SaaS provider, legacy vendor.

[Jason]

Who would that be?

[Chathan]

Like you could get a ticketing system plus a CRM system plus some kind of autoresponder.

[Jason]

What is this Salesforce or Workday?

[Chathan]

You could get Salesforce, you could get HubSpot, you could get Zendesk, you could get piece together lots of these software. And by the way, we invested a lot in these SaaS companies and I invested a lot in these SaaS companies. So they're all great companies. Have a great deal of respect for all the entrepreneurs.

[Jason]

They have good distribution that they build.

[Chathan]

Incredible. But it's the same thing that happened with these cloud vendors versus their on prem rivals. So Salesforce versus Siebel and Oracle Zendesk versus we should get the name. Remedy was like I think the ticketing software before that workday versus PeopleSoft like the on prem incumbents when the cloud software showed up just felt like a terrible piece of software because like they were slow. They like they had all the setup, they were expensive, they were not interactive. Like they couldn't be accessed anywhere.

[Jason]

Like why didn't they just go to the cloud? Like why don't they just code a cloud app and like fix it?

[Chathan]

Because it's really hard. Like I think you've read Innovator's Dilemma and like it applies to technology companies.

[Podcast Host / Narrator]

I actually haven't read it.

[Chathan]

Really.

[Jason]

I mean I know the premise of it, but I haven't actually read the book. Maybe I should actually read the whole book.

[Chathan]

You should.

[Jason]

Page to page.

[Chathan]

You should read that. You should really read it. And you should read also Competitive Analysis. And so it's like these like classic business books. And it's if you look at sort of like if you have a great business that and then you make a technology architecture and you build a large application, you now have built an application that's serving thousands of customers and then you've built a huge distribution force to take that out and sell. And so the business machine that you've built runs on this like things sustaining. So if you need to rearchitect your platform, you need to pause everything, pause distribution, break your architecture, move it to the cloud by the way, and then actually get it to work, have it scale and all that kind of stuff and then teach your distribution networks to distribute this brand new product and then basically start from scratch. It's really hard.

[Jason]

That's hard.

[Chathan]

It's really, really, really hard. The thing that I think people really forget is that when Salesforce was like really growing really fast, Siebel created a product called Siebel on Demand which was their.

[Jason]

Sounds like a not good product.

[Chathan]

Siebel on Demand was the Siebel answer to Salesforce. Okay. And there were people that were covering Siebel at that time that said Siebel on Demand would crush Salesforce. Like that would be the end of Salesforce would be Siebel on Demand.

[Jason]

Were they both publicly traded at the Time.

[Chathan]

Yes.

[Jason]

Okay, so I'm sure Salesforce Stock dropped like 10% the day of launch or something, whatever.

[Chathan]

But like Siebel on Demand didn't work because like the Siebel on Prem product was just so profitable that like, and like Siebel on Demand may have worked as a product but had deficiencies against the Salesforce product. And then Salesforce was, because they had built that company from scratch, was able to distribute it far more efficiently than Siebel could. Like just the company structure was built for selling like licenses for 5,000 bucks or 10,000 bucks to a company. Whereas Siebel was built to distribute licenses that were million dollars or greater. And so you know, Siebel on Demand would come and quote you like a hundred thousand dollars and the Salesforce rep would be like, you can have mine for 10,000. And yeah, Siebel on demand is like 10% worse. And so like, you know, and so

[Jason]

why would you even sign up for.

[Chathan]

That's right.

[Jason]

If you're doing any research at all,

[Chathan]

it's so like that competition becomes so

[Jason]

uneven so, so that the distribution wins. That assumption assumes that they're not going to look at comparable products.

[Chathan]

Correct.

[Jason]

When it's so easy to just salesforce.com click a button, we're in the product.

[Chathan]

That's right.

[Jason]

You can put in your credit card, use it.

[Chathan]

That's right. And so this is like now we're fast forward today with AI applications, we're going through the same thing again. For an AI application to become. For a SaaS company to beat a native AI application, I would argue that they would have to break their fundamental architecture and rebuild the application and redo the business model and reteach their distribution on how to distribute the thing. The advantage of having an AI application company start from scratch is you build this thing AI first from point zero. Like you don't create any code that's not AI friendly. The first set of salespeople you hire and the first set of marketing hires you make are all intended to distribute this like AI product priced as like however you want to price it, consumption seats, whatever. But it's priced like an AI product and you go against the SaaS vendor in these things. It is to me the early signs are astounding how fast these AI applications grab traction and grow and how quickly they're able to displace traditional SaaS vendors. And you know, I think on Twitter or X we're seeing a lot call it Twitter.

[Jason]

I refuse to call it X. I

[Chathan]

think you see a lot of People saying like, you know, the SaaS companies can be really replaced by Claude code. I'm not sure I buy that.

[Jason]

I think of the like, hey, make me Salesforce, make no mistakes.

[Chathan]

That's right. Yeah. Or like build me a billion dollar software company, you make no mistake. 10 billion. Right. I don't think that is what's going to happen. But I do think that if you look at legacy SaaS, their business models are seriously going to be challenged by AI applications that deliver 10x the value for a third of the price. And if you're an enterprise and you can buy an AI native version, you will buy it. I can't see why you wouldn't. You see the experience and it's that stark. And I'll give you a couple examples. If you look at Sierra and you deploy it as a customer service agent, it is absolutely a mind blowing experience. You don't need a ticketing software, you don't need call routing software, you don't need all this stuff to make tickets. Customer issues that are coming into your company go away. It just goes into Sierra and there's a resolution and guess what, your customers are happier. And you're basically replicating your single best customer agent to infinity. That is what Sierra is. So all of a sudden your customer satisfaction goes through the roof. Your business metrics get a lot better, your renewals get better, your expansions get better because customers are actually happy with their experience. And so comparing that with Legacy, which is like chaining together four or five software solutions, it's just hard to really compare them. And then the question then becomes like, why doesn't an existing software person just like do it? Yeah.

[Jason]

Cause I was going to say if I'm Mark Benioff, Salesforce, I went through this, I see why I won and I see like the advantage I had over Siebel. Do I look at it staying, it's like, oh man, this could happen to me. Like I should, I should know that this is coming, shouldn't I?

[Chathan]

That's right. I think that if, and of course, look, I think Mark Benioff is one of the greatest entrepreneurs of all time. He's also been an extraordinary friend to startups and how open he's been with Salesforce, APIs and the ecosystem and all that. I think I only have great things to say. I think that Salesforce should buy a lot of companies now. Like, I think that one of the things that Salesforce has always done really well historically is buy the right companies at the right time.

[Jason]

I was going to Say Slack an example, Is that a bad example?

[Chathan]

No, it's not a bad example.

[Jason]

I feel like that one's been ridiculed. Have like too high a price, maybe higher price.

[Chathan]

Yeah.

[Jason]

I have a friend at Slack, she's like they fucking ruined it.

[Chathan]

They ruined it. Yeah.

[Jason]

She's like not bullish on. Not bullish on Salesforce or like Slack as a part of Salesforce.

[Chathan]

Yeah. If you just look at, you know, Salesforce history, I think people forget that it was founded in the late 90s and just in different waves. For example, when like marketing cloud took off, like they went and bought a great marketing cloud company. Like when they, when Commerce took off, they went and bought Demand Aware. They were making key acquisitions at the right times throughout their growth trajectory and they were actually very good at M and A. And Salesforce Ventures is an incredible investor. They've invested in great companies. They continue to invest in great AI companies. I think one of the things that they should do and of course I'm telling a public company what to do so I have grade level humidity to assume that they would listen. But I do think that if you.

[Jason]

I'm going to cut that part out, by the way. I'm not going to let you say that. I'm going to make you look like.

[Chathan]

I think if you're a SaaS company right now, you should really think about spending 10 to 25% of your market cap to buy AI applications. I really think that's a good idea. And I think if you're an AI application company like Salesforce or ServiceNow or Datadog or whatever, like name your favorite company that's public or private. I think they really should go buy AI applications that they then could feed into their distribution networks or like these AI application companies come in and build them a net new distribution network. I think it's one of those things like if you study the history of software, history of application software, there are moments in time when the on prem vendor should have just bought the cloud thing. The best example of this is BMC which was, you know, ServiceNow before ServiceNow should have bought ServiceNow way before it got as big as it got.

[Jason]

Aren't they like the seventh or eighth biggest software company now or something?

[Chathan]

Of course, but they should like BMC should have bought it. And there were moments in time where I think BMC could have bought it. And like I think if they showed up like they had the market cap to buy it, they had the wherewithal to buy it, but they didn't because like you Know, it was like, well we're trading at X revenue multiple and I don't want to give sales for or ServiceNow, you know, 20x revenue multiple, whatever, like. But in retrospect it was, you know, completely changed the game. The other one is like, obviously, you know, Salesforce. I mean there were times when like it was rumored that Microsoft was going to buy it. There were rumors that like, you know, could Oracle ever buy it? Like, you know, like Salesforce just sort of like completely cleaned out CRM from all the on prem vendors and like all those businesses just ended up going to zero. And so it could have taken Salesforce out much earlier in its journey. So if you just look at every big category winner in SaaS, there was an opportunity for the on prem company to make a big acquisition and make something of it and they didn't. And I think that if you're watching the AI application thing happen, you're starting to see MX sort of pick up. But it's not really at the pace that I would encourage these companies to think about it, which is just like you really should jump into this game and buy some of these things because companies are about to get gigantic. They're getting to 100 million. I mean Manus went 0 to 108 months. These companies are getting really big really fast. They're going to 100 million really fast. Once they're getting to 100 million, they continue to scale beyond that. There's lots of questions about margin profile and all this kind of stuff. And I'm telling you, these AI application company P&LS, maybe they don't look as pristine and as predictable as super mature SaaS companies. But the early days of SaaS companies, those P&Ls didn't look that pristine either. People just should go look at the workday S1 and look at the gross margin of workday in the early days. So interestingly the software had around 80%, 75%, 80% gross margin. But they were selling services to implement the software at negative gross margin. So blended they had gross margin. Some years in the 50s or even below 50, that was fine. And I think there was one year, if you go to pull up the S1 either it was the first or second year they had negative gross margin,

[Jason]

which is fine because S1 you're going public as a software company.

[Chathan]

Like one of their out original years

[Jason]

was like, we have negative gross margins.

[Chathan]

Yeah. And then it got better obviously. And then you know, like the year, the year right before they went public, they had great gross margin but like, you could see that evolution in that S1. And so to me, it's like, if you've been around software long enough, you've seen some of these patterns before. It's like, do not complain about a negative gross margin software company. If you're seeing the patterns that you saw in the last phase, which is like people are implementing these things, treating these things as systems of record, whatever, there's some kind of gravity around the workflow or the data or whatever, and these things end up becoming a core part of a business. They're not getting ripped out. The only way that business account goes to 0 is if the sort of business customer goes bankrupt or goes out of business. Otherwise they're paying for this piece of software. It becomes that essential to the business. And if you look at that kind of trend, it's like, yo, this is happening. And I think you're going to start seeing the first set of S1s for these AI applications 2027, 2028. And I think people are just going to be really surprised at how much these companies look like software companies. It's like, yeah, they look like software companies. And then instead of paying a ton of gross margin to the cloud vendors, we're just paying a ton of gross margin to inference providers. We're either paying OpenAI Anthropic, Google for paying Core Weave or Fireworks for inference tokens. That's where the cost of goods is going and that's okay. And then companies get better at optimizing that and getting more efficient. And so it's a real wave. And I think the private markets have fully realized this opportunity. And I think this is why you're seeing application companies. It's a very attractive category for venture today. But I'm not sure that the public markets have quite embraced this. And I'm not sure public companies have quite embraced this.

[Jason]

Why not? Because if I'm public market CEO, I look at my stock and I trade like three times revenue or whatever, and I'm like, these fucking kids are getting like 200 times revenue.

[Chathan]

That's right.

[Jason]

And they're so small, maybe they're like, they're growing fast, whatever. Like why? Why do you not think they pulled the trigger on some of these acquisitions?

[Chathan]

Well, I think it's human nature. I mean, imagine you're a software company that's run the company for a long time and you're trading at three times revenue as a SaaS company and you're like, okay, I should go buy the AI version of this thing. And you have to now pay 20 times revenue, 50 times revenue. You're at 90% gross margin. Your AI alternative is at like, I don't know, let's say 20% gross margin. That's a pretty sort of bad deal.

[Jason]

Yeah, it seems pretty stupid the way you just described those numbers.

[Chathan]

That's right.

[Jason]

Sounds not good.

[Chathan]

So imagine being in that room where you're trying to pitch that deal to your management team or to your board

[Jason]

or to yourself and it's probably 20% of your market cap or something like that where it's like a, almost a bet the farm. It's probably close to that threshold.

[Chathan]

This is. Again, I would just ask people to look at the public markets of 2012 and look at where SaaS companies were trading then and compare them to their on Prem rivals. SaaS companies were trading at like 20, 25 times revenue. If you just look at where ServiceNow and Workday went public, they were trading at the time and you could just look at their coverage of these valuations. People were calling them absolutely insane. That's what they were calling them. And Salesforce was always considered an ultra expensive stock in the beginning. So was ServiceNow and so was Workday. They were all considered like wildly overpriced.

[Jason]

Well, a lot of it too is just they don't have any cash flow profitability. Like if you're straight up the financial statement just like, oh, like according to the statements, free cash flows, they're trading at 800 times free cash flow. It's overvalued.

[Chathan]

Sure, all that is fair. But the interesting fact on ServiceNow actually is that they were cash flow positive from like year two or something. Something outrageous. It was like an ultra efficient business.

[Jason]

But most good businesses are, yeah, they're

[Chathan]

becoming very capital efficient. Like Salesforce was very capital efficient too. So if you just look at 2012 is a case study. Look at where SaaS companies were trading and look at where the on prem vendors were trading and we just, you know, I just talked about how like those on prem vendors should have bought the SaaS companies. They should have just paid 20 or 30 times. That would have been the right business answer.

[Jason]

Yeah, I've heard actually from Benioff is like he would have sold. It was just he needed a 40% premium and people would only offer him 30%.

[Podcast Host / Narrator]

He just never sold.

[Chathan]

There you go.

[Jason]

Because he never got the price he wanted.

[Chathan]

There you go. And so there were good deals to be had. But at the same time they look like bad deals to the on prem companies because it was like I'm trading at 20 times free cash flow or 2 times revenue. And you want me to pay 25 times revenue and 800 times free cash flow to buy this, like, SaaS thing?

[Jason]

Like, it seems like SaaS could be

[Chathan]

a fad, 100% SaaS could be a fat. AI could be a fad. So this is where you get into the circular thinking of not doing the deal and you just get stuck. And I think if you just play this out, these AI application companies were much cheaper to acquire in 2023 than they were in 2024, than they were in 2025, than they're going to be in 2026, they're going to be in 2027. And it's happening, it's happening right in front of us. We're just seeing this happen. And I have to tell you, from a venture investor perspective, it's a really fascinating cycle to live through because I was an investor in the first cloud cycle and I would always think, why aren't these people making the move? They should buy these SaaS companies. This is obviously the logical thing to do. And here we are again, for me, sitting in a second cycle and I'm saying the same thing. And it's really interesting that the SaaS companies have forgotten their own state that they were in. They have forgotten their own position in 2012 and 13 and 14 and what it would have taken for an incumbent to buy them. They are now the incumbent and not sort of embracing what it takes to buy the upstart.

[Jason]

So if I'm an upstart, if I'm the founder of an AI company and I just heard everything you just said, why would I sell? Because I'm going to beat the SaaS companies in three years. Yeah, you don't need bigger than that. Why would I sell to them?

[Chathan]

That's right. I mean, if, if you, you know, like you mentioned some founders just know, you know, there's the number and you know, there's all these famous stories about like Google named a price to Yahoo and Yahoo said no, Yahoo could have

[Jason]

been like the largest company in the world, could own Google, Facebook.

[Chathan]

Right.

[Jason]

I bet they talked to Amazon.

[Chathan]

It's like, you know, there's a story of like Facebook and Yahoo, something like Yahoo ordered a billion and then there's some kind of counter or something, whatever. Like there are these famous stories of like.

[Jason]

And Zuck was like, well, what would I do if I sold? I would just start another social network.

[Chathan]

That's right.

[Jason]

So why would I sell it?

[Chathan]

So there Are these like notes in history where companies, they named a number to the incumbent and said like okay, just have to get here. And of course in some cases the incumbent did get there and that's how you have giant SaaS acquisition or the incumbent didn't. And then those companies went on to be independent and got gigantic. And so I think that some founders may just have a number in mind that if the incumbent hits like 40% premium to their current stock price or whatever, that might be attractive. But I think what I'm surprised by is how few people are trying. I would have expected many of these doors to be ma people all over those companies being like, what would it take?

[Jason]

Do you think? Part of it is there's so much late stage capital that if I'm a founder it's, it's not as hard as it maybe could be or should be to fundraise. And yeah, I could take a deal, I could sell to Salesforce, but also there's 18 people that are giving me $100 million. And to keep going, it actually makes that easier to stick on the path.

[Chathan]

Absolutely. The private markets have gotten way bigger today than they were in 2012 and 2013, 2014. So that makes it much better. I think the other part of it is going public has gotten harder for companies and I think there's a lot harder.

[Jason]

It's the same thing. Yeah. I mean, what's so hard about it now?

[Chathan]

So the difference between today and even 2007 and eight is the number of things you have to do to be a public company. There's just more to do now. Is it really difficult? No.

[Jason]

You should just hire a couple more people.

[Chathan]

Yeah, that's right. It's not that hard. Hire a couple more people, pay a couple more consultants and they'll do it for you. And so what I think is going to happen, and I think you already see it from the bankers, is there is a growing demand from public software investors. Software PMs.

[Jason]

Because they're looking at their universe and this thing's shrinking. That's right. What is up with this?

[Chathan]

And so they're, you can already tell like from our conversations with investment bankers, they're already telling us like the software investors are telling us to bring them the AI application companies. And so for the first time in a long time you're starting to see investment bankers talk to companies under a hundred million dollars of run rate saying do you guys want to start doing non deal roadshows where you start meeting like PMs of public software investors? This is quite a shift. A couple of years ago people would say, oh, you need 500 million of ARR before you can talk to any public investors. Because if you don't get there, nobody wants you to go public. Very different. Whereas we just had a conversation with a banker who wants to organize a non deal roadshow for one of our companies. The company's not yet at 100 million because it's fundamentally really interesting technology. And there is a great deal of demand from public software investors to meet these companies. And for the first time in probably a decade, I'm hearing bankers say things like, yeah, 100 million ARR. We could probably take that public. Haven't heard that in a while.

[Jason]

Really. And it's probably just because, I mean it's really the companies are growing fast and that's all investors care about. They just want you to grow as fast as possible.

[Chathan]

That's right.

[Jason]

In a semi healthy slash will be healthy at the end state.

[Chathan]

That's right. I think if you just look into the public markets, how many companies in software are growing greater than 30%?

[Jason]

It's like zero.

[Chathan]

That's right. Yes.

[Jason]

You can't be growing less than 3x to raise a series A in venture land. There you go. If you're below 3x your growth, I would say it's probably better to get the growth rate up, figure out how to grow faster versus spending time.

[Chathan]

Actually, I think it's the 3x thing is probably overdone. I think if you're growing like 2 1/2 x still pretty okay, well fair.

[Jason]

But there's a lot of private Companies double from 50 to 100 that would be really attractive.

[Chathan]

And there are a whole bunch of private companies that went 50 to 150. Yeah. And it's just there's these public market PMs.

[Jason]

They're like, give me that. Like I want that.

[Chathan]

That's right. That's exactly what they want. And they know because, you know, public market PMs also are stepping into private markets and meeting these private markets companies on their own and saying like, wow, there's like a ton of growth in these companies. And if you're sitting here as a software pm, you're looking at your universe of software companies that are available to you as a public market investor. If I was sitting in that seat, I would be demanding the bankers bringing me give me this better application. Because again, if you're a software PM that invested through SaaS, and I was talking to a hedge fund manager who was at one point through SaaS, he was telling me he was long on 100 software names.

[Jason]

Oh, okay. And it's probably all growing, like, 50% a year.

[Chathan]

He was like, we, you know, he went long Software starting in 2010, and he just decided, like, this is clearly the future. And like, every time a software company came public, like, he figured out a way to enter that company, and, like, they were extremely successful, et cetera. And his comment to me is, when are you bringing your AI application companies to the public market? We need those in the public market because we're completely starved for growth, and all the growth is being just taken by these AI native companies. They're all taking all the growth. If you just look at net new ARR added, where is it going? It's really just going to all these AI companies.

[Jason]

I think maybe you saw this stat. You might know what I'm talking about. Since ChatGPT launched, I believe this is about a quarter ago that I saw the stat that OpenAI and Anthropic added as much revenue as every single publicly traded software company. Did you see the stat?

[Chathan]

I buy that. And it's.

[Jason]

I mean, this was three months ago, so it's probably even bigger now.

[Chathan]

Right. And so, you know, I think, you know, there was press about OpenAI revenue that has gone from like 6 to 20 billion this year. I mean, that's a lot. That new 14 is a lot.

[Jason]

Yeah, well. And part of the argument, though, for some of these AI companies is like, oh, the valuations are so high. How do you square that up then? If you're thinking about what am I investing into? Maybe you're doing a Series A, you're doing a seed round, or you're doing. It's a public company. But how do you justify the higher valuations on some of these companies?

[Chathan]

I think it just depends on your fund size and your strategy. So we have a very specific fund Strategy. We're a $500 million fund with four equal partners. You know Eric, you know Ev, you know Peter. You know me.

[Jason]

I actually don't know Peter. I've never met Peter before. Well, I want him on a podcast. It'd be cool to meet him and have him on sometime.

[Chathan]

So it's four of us, and we're investing in seed and Series A companies. And the primary goal of each of our investments is that we want to be the primary board member for the company. That's the goal of every investment. And if a company's not looking for that, then we kind of don't have a role to play. And so the Valuation, frankly, is not the governor in our investment decisions. If you were to be a fly on the wall in one of our partner meetings, the discussion really isn't about the valuation or the deal structure. We don't spend very much time on that at all. That conversation is really about the company. And does the partner that's advocating for the company want to work with that entrepreneur? And do the rest of us want to work with that entrepreneur, too and help them support and build something really meaningful? And oftentimes, what you'll find in our conversations is that when an entrepreneur, one of our partners, is excited about your company, you'll quickly find that the other three partners encourage you to lean in. I think this is where our incentive structure really helps, because we all share economics equally famously. And so if my partner is excited to work with an entrepreneur to help them build something big, I want them to go do that. It's like, yes, please go invest and

[Jason]

go make me money.

[Chathan]

Yes, 100%. And so our incentives are fully aligned on that. And so when somebody gets excited, it's very clear the firm helps rally and helps them gain elevation, helps them finish that investment. And so to us, that's the governor. And then the other side of that is also by having four partners, each of us probably has capacity to do two investments a year. And so as a fund, we're doing, call it eight, nine, maybe ten investments a year.

[Jason]

Someone gets really excited in the year.

[Chathan]

That's right. And so that means that you have, on any given day, sort of like your time as the governor of where do you want to spend time with? Who do you want to spend time with? And so that's ultimately it. And that's our strategy. And so that means that we have decided that that means that there's a typical investment size and a typical ownership that we'd like to go for. And of course, we're very flexible on that. It's like, there's no rules. We don't have written rules that say, like, we're only going to do it if we get this much or that much.

[Jason]

The classic venture model is like 20% series, a $15 million check or something like that. Or maybe, I don't know.

[Chathan]

When I started in the business, it was a little bit less than that.

[Jason]

I don't know. It's all over now.

[Chathan]

Yeah, we're doing something.

[Jason]

I saw 4 million, $4 billion seed round the other day.

[Chathan]

There you go. Yeah.

[Jason]

I don't know, like, what is this anymore?

[Chathan]

But I think for us, to be clear, we still have those rounds where you can write small checks and get meaningful ownership. You're just like, this is part of the incubation effort that we have. So we have EIRs, we're like helping incubate companies. And I think those opportunities still exist when you're building relationships that early. And then there's certainly companies that are much further along that have a little bit of traction or whatever and they're commanding a different market price and that's okay. And for us, as long as we have a relationship with the entrepreneur and can serve on the board, that's cool. We're flexible on that.

[Jason]

So what's the most untraditional kind of venture round? When I'm a venture purist and I would scoff, what's the one you think they would be the least characteristic?

[Chathan]

You know what's really interesting is that if you look through the history of Benchmark, there are times when Benchmark did these non traditional investments. So if you look at the Internet era, I don't know if you know this, but like Nordstrom spun out Nordstrom.com and Benchmark invested in that corporate spin out as an example.

[Jason]

I know you guys invest like Jamba Juice. That's probably the craziest one I've seen.

[Chathan]

So if you want to scoff at sort of like traditional venture, there's examples like this throughout our history. And perhaps the most famous that I remember because I was just entering the ecosystem then was when Benchmark did the growth round at Twitter. That was like a very unusual move for Benchmark at the time.

[Jason]

Was it like a series C or something?

[Chathan]

Yeah, that's right. And it may have been a series B or a series C. And that was considered a late stage round at that time. And it was very unusual to see a very early stage firm like Benchmark do that round. And so I think the thing that people like this narrative of somehow there was only one set of ideal deals that Benchmark has ever done since the founding for 25 years. And all of a sudden the model has to change. It's like, no, the model has always been you have a small set of partners working with companies that they really want to work with, a set of partners that really want to back a certain of fundamentally game changing ideas that end up becoming really large standalone companies. And that idea then has resulted in lots of flexibility on the other side of what does that structure look like? And the one thing that we haven't done is created a fund of family of funds and we haven't gotten big as A partnership. We've continued to be small.

[Jason]

You kind of. There was an era, like, early Benchmark. You went to Europe. I remember you had, like, an Israel fund, maybe, too.

[Chathan]

That's right. There was Benchmark Europe, Benchmark Israel, Benchmark us. And, you know, I was in the venture business then. Like, I wasn't at Benchmark, but yeah, Benchmark had. And then decided to get small again. And we've been small since.

[Jason]

And you think that was the right move? Get smaller?

[Chathan]

Absolutely. I think now other people have built incredible franchises by getting big.

[Jason]

Yeah, there's people that do more in management fees every year than your fund size.

[Chathan]

That's right. So multiples of our fund size in management fees. I think it's a great business. And I think you'll eventually see venture firms that are public, too. I think that's okay. I think that's great. I think ultimately you have to come back to the partners themselves and what kind of organization do they want to be part of? And for us, we want to be a part of an organization where you could do deals like Manus and Sierra and Lagora and Fireworks and LangChain, all in a span of 12 months. All of those deals are completely consistent with how we want to practice the venture business. When specifically in all of those, you have a benchmark partner partnering with a founder, joining the board, and working with the entrepreneur to create a really big business.

[Jason]

Do you think that is maybe like, an outdated model of, like, that we must own 20% in your series A? Like that. That approach of, like, I have this rigid portfolio construction based on the rules that have become memes over the. Over the years, whatever. Like, is that not a good approach to venture anymore?

[Chathan]

I think everybody should approach venture however they want to approach it. I have, like, everybody's on their own journey. Everybody has their own strategy.

[Jason]

This is like, the most complicated. But I mean, I've heard, like, Eric told me he's just like, we're trying to find people building the best companies and just be there and own a part of it. And that's how you make money.

[Chathan]

And I think, like, look, there is a model that works really well, which is the YC model. They have a very specific structure, specific amount of money. There's an incubator program, and there's a specific ownership on the other side of that. And I think that's a fantastic model. And I think YC is, like, great value for founders. Like, I like, whenever founders ask me the question of, like, should we do yc? I say, absolutely, yes.

[Jason]

A good chunk of your personal portfolio companies have done yc.

[Chathan]

That's correct. Yeah. I am a huge fan of yc. I think it's a great program. I'm a big fan of the partners there. And so I think YC has a very specific structure. There are other incubators and other early stage funds that have very specific things that are like, we only want to do deals that are this specific tech size, this kind of ownership. And I think that's a winning strategy as long as you don't have FOMO and you very specifically focus on. I only want to do these kinds of investments. Okay, great. But the way we're structured is we're generalists. We're a group of generalists and we want to invest in really exciting companies. And look, the last investment I did was a crypto company.

[Jason]

Oh, this is fomo.

[Chathan]

That's right.

[Jason]

I was like, what? What the hell is this?

[Chathan]

I was like, what?

[Jason]

You're like enterprise. I've always thought it used like enterprise software.

[Chathan]

That's right.

[Jason]

Manus consumer AI.

[Chathan]

Yes.

[Jason]

And crypto.

[Chathan]

Yeah, consumer crypto. So if you look at the two investments that I made in 2025 were both consumer apps.

[Jason]

So are you rebranding? You're going through.

[Chathan]

There you go.

[Jason]

Like a Phoenix moment of like Enterprise SaaS has been burned to the ground by the public markets.

[Chathan]

But I think fundamentally, if you go back to again, what is that motivated by? I just thought the entrepreneurs in both cases were extraordinary. And as a generalist, you kind of understand what they're working on. You have a deep appreciation for the product they're building and how they're approaching the problems. And honestly, I just wanted to work with both of them. I just really think at fomo, it's Paul and sei, they're amazing. They're just extraordinary entrepreneurs that want to bring a totally brand new experience of crypto to consumers. You're, well, worse than crypto.

[Jason]

I mean, I don't know, I'm like, it's all a scam. It's just people scamming you.

[Chathan]

So what's the different thing?

[Jason]

Are they scamming you in a more polite way?

[Chathan]

Absolutely not. Crypto is. To me, the way I think about crypto is that it has a huge on ramp and there's a big barrier to entry. I think that's part of why there is fraud and scams is because it's very hard to onboard onto crypto and very hard to manage crypto.

[Jason]

Yeah. I remember buying my first nft. Everyone's like, oh, this is the future. It's so easy. It took 30 minutes, 100% get MetaMask up. And I bought this and I was like, there's no way.

[Chathan]

That's right, no way.

[Jason]

This is like your plane ticket is an nft.

[Chathan]

This is not. The experience is so frictionful that. And then it's very easy to analogize back to my own personal experience, which is like crypto to me has been hard for me to experience as a software investor because it was such a frictionful experience to get crypto or to get an NFT or to experience anything on chain. And I met Paul and Sei and they told me to download the FOMO app and try something and I did. And it was like consumer grade. And I was like, whoa. And there's a built in social graph and all this kind of stuff with ugc. All these kinds of elements that are very classically software just apply to a new industry.

[Jason]

If I search FOMO in the app Store, will it come up?

[Chathan]

Yeah, absolutely. And I think that these are the kinds of things that I look for.

[Jason]

It kind of begs another question of what do you or what do you feel like benchmark looks for in founders that you're trying to back? We've talked a lot about a bunch of different stuff, but if I were to sound bite this, what is it that you guys are looking for?

[Chathan]

The through line for the entrepreneurs that I've worked with is that they have some deep insight on the problem they're really passionate about. And it could be just like seriously going backwards from the investments I've done. It's like, could be consumer crypto, could be consumer AI, could be legal AI, could be document processing, could be sales tax, it could be stablecoins, it could be payment Rails, it could be integration software. All of these things. The common line in all of them is I'm typically investing seed Series A. Usually there's no product, usually there's no revenue, usually there's no metrics. For example, Manus, I did pre launch, this was a beta product. And there's some deep insight that the founder has, some deep perspective that the founder has. And intuitively as soon as I hear that, you're like, yes, that's absolutely obvious and that's how the world should function. And when I hear that, I want to work with those founders and for me that's like the number one thing above all else. And the great thing about being an early stage investor is you get to go with these founders on these journeys and then it's okay. If it doesn't work, the thing that is a mistake as an early stage investor is missing out on the companies that work, not investing in companies that don't work. If a company doesn't work, it's okay. It's a 1x error. If a company works, it can generate

[Jason]

a lot of returns.

[Chathan]

Yes, that's right. And so you just want to be in companies that work and so you don't worry about the downside. And so when you have this view, as I do, that you just want to work with founders that have a deep passion for a sector or a problem and have some unique insight as to why that opportunity is now available and why they're uniquely positioned to address this problem. I want to back up.

[Jason]

Maybe this is an interesting, probably last thing we can talk about. You told me that one of those interesting insights was with Codegen things that you saw some stats. It's also a company where they publicly, there's negative gross margins. There's people like, oh, these companies are. You read some of the consensus maybe a year or two ago, it's like six months and these things are going bankrupt. Right. So what was the thing you got excited about there? And then how the margins, how did you get comfortable with that?

[Chathan]

Look, we're investors in cursor, I think code generation. And look, one of the Manus primary use cases was also code. I think that we're very early in how much code can be generated for the world. And two, the thing that surprised me is how much demand there is for CO generation across consumers. B2B prosumer. There's just massive demand for CO generation products. I do think that the margin question at the moment is a little too early to make a final verdict on. We don't know. And the thing that is happening that you may have seen, for example, is like where Eric is on the board of a company called Cerebras, which is a very specific AI chip that speeds up inference. Once that chip sort of starts to propagate and you start to see AI technology run on that chip as an

[Jason]

example, it's like an AI native chip, right?

[Chathan]

That's right.

[Jason]

Where it's like, it's like built for running AI models, workflows on top of it.

[Chathan]

So inference goes way faster on a Cerebras chip as an example. So if you speed up inference dramatically. So the thing we don't have yet is we don't have AI specific chips beyond Nvidia. We don't have AI specific clouds. We're starting to get that. We're starting to get AI chips, we're starting to get AI clouds like fireworks. We're starting to get AI infrastructure built. Once all of that gets built, then we're going to start to see a stabilization of the infrastructure parts. And then only then are we going to actually understand what the gross margin characteristics of these things are going to be. But right now I think it's too early to judge the P and Ls of these things. All you can actually just get a sense of is the consumer prosumer and B2B demand. And right now we haven't hit the ceiling of that demand. The more we produce coding models, the more we generate code, the more we make code generation faster or more efficient or more accurate. There just seems to be more and more pull of it. And I think if you just look at the amount of revenue generated by co generation, it's gone zero to a couple billion really fast. And you can count that at the inference layer, you could count that at the application layer, whatever you want. It's probably the fastest growing software market in the world right now, so you can judge the demand side of it. And I think it's like way too early to understand what the long term margin characteristics of this sector is going to be.

[Jason]

Is there anything else you want to talk about at all?

[Chathan]

No, it's perfect.

[Jason]

I had a bunch of other stuff. I know we got to get.

[Chathan]

Sorry.

[Jason]

I probably need to eat this banana before we cut the film. Actually. Do you know how to break a banana in half? Have you ever seen this? Wow.

[Chathan]

Have you ever seen that before? No.

[Jason]

Yes.

[Chathan]

That's amazing.

[Jason]

Yeah, it's my. One of my skills in life is opening.

[Chathan]

Amazing.

[Jason]

Well, it's a lot of fun. Thanks for.

[Chathan]

Thanks for having me. I'm good.

[Jason]

That's a good place to cut it right there.

[Chathan]

Yeah.

[Jason]

You refusing my banana offering?

[Podcast Host / Narrator]

And thank you for listening. A quick thanks again to Numerolon Flex for supporting this episode. Put your sales tax on [e-mail] and upgrade to Flex Elite to get $1,000 on your first card using code turner at the waitlist link in the description. If you enjoy this conversation, please like comment, subscribe and share with a friend who runs a publicly traded software company that should buy an AI startup. Make sure to check out the back catalog of over 100 episodes with the founders of companies like Robinhood, Mercury Box and some of Chathan's portfolio companies like Numero and Reducto. Tune in over the next few weeks for guests like Mike and Nikhil at Footwork and Scott Stevenson at Spellbook, the fastest growing AI company in Canada. If you don't want to miss any of these, subscribe to my newsletter. The Split linked in the description to get each episode plus a transcript emailed directly to your inbox every week. Thanks again for listening. See you next time.