CodeRabbit CEO Harjot Gill on software's new bottleneck and zero to $50m ARR in 2 years

Harjot Gill is the co-founder and CEO of CodeRabbit, the AI code review company that became the most installed AI app on GitHub and GitLab. Over about two years, CodeRabbit went from zero to over $50M in ARR and a $1B+ valuation. It is his third startup. In this episode of Summation, Harjot and Auren discuss: Why the bottleneck in software just moved from writing code to reviewing itWhy mature companies now adopt AI firstWhy you should never let a coding agent review its own workThe playbook ...

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  What AI is doing is like taking the best practices it has learned to an average person. For example, I can do marketing and may not be the best marketer, but it does level the playing field for me. So we're seeing that imbalance where sometimes even the senior people are being held back because they're too afraid to let go. Right now, I would say is the best time I've seen in the history to do startups and create value.

• 0:24Auren Hoffman?

  Hello, fellow data nerds. My guest today is Harder Gill. Harder is the co-founder and CEO of CodeRabbit, the AI code review company that became the most installed AI app on GitHub and GitLab. Over the last two years, they've gone from zero to over 50 million ARR and over a billion valuation. Harder, welcome to Sumation.

• 0:44Speaker 00

  Yeah, thanks for having me here, Arin. Really excited to be on the show.

• 0:47Auren Hoffman?

  Really excited. Now, just a full disclosure, Flex is an investor in CodeRabbit. We invested in the seed, we invested in the A, we invested in the B, we're about to invest in the C. We love CodeRabbit, so that's the full disclosure before we get going on the show. Now, AI is writing more code every month. And I think the bet that you were making is that the bottleneck is going to move from like writing to reviewing. How has that played out?

• 1:18Speaker 00

  And it played out even in more interesting ways than we imagined it would play out like two years back, especially with the capabilities of these agents improving and leapfrogging every few months. Now we're seeing like these agents are becoming more capable, able to run for longer periods of time. And recently also, we are now seeing like the adoption of background agents take off. As you can imagine, like the code is getting generated, not just from CLI and your code editors. It's being also being generated by Linear. Yesterday, Notion announced that they will be writing code. Data Dog wants to write code, Posthog wants to write code, almost like the floodgates opened.

• 2:00Speaker 00

  And now you have this deluge of pull requests that are getting created, right? So the human attention is now all about how do I validate? And how do I decide and build trust in whether the changes that we're seeing from these coding agents can be shipped safely or not? So yeah, I mean, it's pretty interesting. Like, I mean, when we started, that time was just AI co-pilots. You were like doing tab completion, the GitHub co-pilot, Cursor came in a little bit around the same time as CodeRabbit started. So the velocity was very different and the code review was still like very much human in the loop. Like humans were reading CodeRabbit comments and going and addressing those comments.

• 2:42Speaker 00

  But increasingly, what we're seeing is now we are part of the loop, right? So it's just agents reading the comments from this adversarial review that we provide and get to a really good implementation before some things get shipped.

• 2:56Auren Hoffman?

  And it does seem like even most organizations that are of any size now, and once you get past maybe 10, 20 engineers, they're using many different systems to write their code, right? They've got Codex, they've got Claude Code, they've got Cursor. They've got many, many other systems that are, and as you mentioned, you're using Notion. Now, all these other folks are helping them write code. And so there isn't like one system that is like the main input to their code. Is that correct?

• 3:28Speaker 00

  That's right. I mean, this has become more of like a taste thing, just like every other tool in the developers. That have in the past, as you know, like it was about WIM versus Emacs. A lot of the editor wars, now we're seeing the harness wars or the model wars, like you have cults forming. And it's really about the choice of the model, like people have built fondness to Opus versus pragmatic personality of GPT-5.6 or 5.5. And then it's about the surface and the harness where you want to consume these models. So it can be in the IDE, that's still like where Cursor is still very popular as an IDE solution. Then in the terminal, you have Claude Code CLI.

• 4:10Speaker 00

  And now very recently, we have seen a lot of developers, including myself, use desktop applications where Codex desktop app is kind of ahead of the pack. So yeah, so it's kind of like what the organizations are now doing is like giving developers freedom on which model, harness, surface, they want to consume these agents.

• 4:29Speaker 00

  And then when it comes to code reviews, that's where you want to centralize as the guardrail. While you have proliferation of these coding agents, everything has to go through a single choke point, which is now CodeRabbit and your GitHub and so on.

• 4:43Auren Hoffman?

  I personally like when I, even when I'm in the same repo with the, you know, on my GitHub, I will sometimes use Codex and sometimes use Claude Code for different reasons. And I don't know, sometimes I get excited about one versus the other, or it could be like, so one week I'm using one and the next week I'm using the other. And so even like one person may have like in some ways weird personalities and moved around different systems.

• 5:09Speaker 00

  No, that's interesting. We have seen that with our team as well. Like, for example, like when it comes to Jit Chat and like planning, like you will find Claude Code models to be more curious and guide you in the right direction, whereas the OpenAI models are more like, you just tell them what to do and then they'll, no questions asked, they will just go and do it, right, what you ask. So yeah, I think people are also like seeing, mixing the coding agents for various reasons because the capabilities do differ across different model providers, the personalities of these tools.

• 5:39Auren Hoffman?

  Or you just run out of like credits in one and you switch to the other for the rest of the week and then you jump to the other and it's like, you know, and it's like, and they, you're kind of like in there doing it.

• 5:49Speaker 00

  That's right. I mean, right now, we are also living in the time when OpenAI and Tropic are kind of testing the market on this hundred, $200 plans, which they feel that would be the most stable pricing point for these coding agents. And currently, they're subsidizing, so of course there are like some rate limits. But as you know, OpenAI keeps resetting and so on. And at the same time, the, once you're hooked to these coding agents, like there's a lot of anxiety, like a lot of the developers are feeling so empowered that the first thing they're doing, waking up, is like looking at their coding agent and that's the last thing they do when they're going to sleep. They're just keeping these systems busy. And that's where like, a lot of people are like switching harnesses and buying multiple of these like hundred, $200 plans.

• 6:33Auren Hoffman?

  Yeah, yeah, yeah. I personally have two $200 plans going and probably may add a third one at some point. Like, how does like organization think of it? Because like, I imagine when all these people committing, all these people doing, you know, committing different types of code, they're, they want to have some sort of better, some more standard review process, right? Then in each organization, they want to have some sort of centralized decision of how they should do reviews, what's important to do reviews, how much should they think about security versus speed versus, like, how does, how does one think about like the review process as an organization?

• 7:14Speaker 00

  Yeah, I think people are looking at like SDLC as a whole. Like, if you look at the software development lifecycle, like pre-gen AI, there was a very distinct planning phase because downstream, your engineering resources were very expensive. So you needed to be sure that what they were building or working on at the same, at any time was the right thing to work on. That's where you had Linear, Jira, Notion, a bunch of these planning tools come in, right? And downstream, then you had code reviews because you had, these are the outer loops, right? So they were always collaborative. So your planning was collaborative, then the developers will work on their machine and then the reviews were always collaborative, right?

• 7:55Speaker 00

  So as soon as the middle inner loop became fully automated, now we can go from a prompt to an implementation which is thousands of lines, within like few minutes or hours, right? The collaboration has broken now, right? So agents are good at writing code, but the collaboration is what suffers, right? So you have now processes broken upstream and downstream, planning is being bypassed increasingly. So there is no reason to track something and you can just go from an idea to implementation without going through Linear. And that's why Linear is also saying issue systems are dead, they wrote a blog about it and they are like going downstream into code reviews themselves.

• 8:38Speaker 00

  And code reviews become the only choke point because really the floodgates have opened. There's no process or choke point upstream. You can't really tell these agents to get an approval before opening a PR, I mean, that work stream, that system of plans or approvals does not exist today, right? Git is the only approval-based system that we have, the workflow around that, the pull request model, right? So naturally, like everything's now hitting GitHub and no wonder GitHub is fearing to scale as well, just the PR volume has increased so much. So it's very interesting, like the review process when companies look at it, they clearly see That becoming a pain point, even pre-Jenaia, it was a pain point around twenty to twenty-five percent

• 9:21Speaker 00

  time, every software engineer, especially the senior talent would spend on code reviews. Now it's even more than that, much more. And in fact, to a point, I'll tell you, like, when we enabled background agents in our code base, like a few months back, we launched a Slack agent where, which could respond to data dog alerts or support tickets. We have over one thousand pull requests open in GitHub right now, even though we are like automating the reviews, but even deciding what to ship, what to spend time on, like processes are broken, our C I C D was broken. So it's not just the review, even like downstream, how do you build, validate? So a lot of things suddenly became, hit the breaking point as the middle loop in the loop got automated.

• 10:02Auren Hoffman?

  I built this like super silly app for myself. And even when I built this app, I had, I had to use, I used Claude Code, then I used Codex, I had GitHub, I had Superbase, I had Vercel, I had Modal, I had Resend, I ended up using Code Rabbit as well, I had a bunch of other tools, just to build this like, kind of silly app there. Like, how do you think about going across all of these different things? Because it's simply, it's like, yeah, there's the code, which might sit on GitHub, but it's interacting with Vercel, and you have to understand what Vercel is, and then it's interacting with the Superbase, and you have to understand, okay, well, what is the, the good, you have to understand, like, okay, how does this work with Superbase? Because it might not work with something else.

• 10:46Auren Hoffman?

  And then it's interacting with Modal, which has this, like, so how do you think about, like, these, like, tool ecosystem that, that kind of interacts with the code?

• 10:54Speaker 00

  Now, that's a great question. In fact, now with the agents, you can pretty much automate a lot of the DevOps and operational PCs as well, and it's really now a matter of bringing the right tools to the agents. It's about the surface area they can access, right? And right now, if you look at the model capabilities, they're right there, but the products that capture the capabilities are still lagging behind, right? To the point that it's really the model labs pushing some of those capabilities by adding plugins in Claude Code and so on.

• 11:23Auren Hoffman?

  Yeah, sometimes it's like, okay, we're stopped here. You have to go into Superbase yourself and then, you know, put this little API key in there, go into Vercel, and it's like, oh, great, like, I didn't know I had to go log into UI to go do this, like, yeah.

• 11:37Speaker 00

  Yeah, providing access to these agents is the challenge right now that everyone's trying to solve in many, many ways. Right now, most of the solutions, the reason they're successful locally is because developers are able to do a local OAuth, run an MCP or a plugin, and provide their own credentials, right? And which means that if you have, like, a company of hundred engineers, roughly, like, I don't know, ninety percent are probably not even connecting the right tools and not seeing the, the fullest extent the agents are, how successful they are in accessing Vercel or GCP and other tools, right? And that's where the enterprises are still, like, lagging behind these capabilities because you don't have the infrastructure. For example, you need sandboxes, and that's not enough.

• 12:21Speaker 00

  You don't want the agents to see those API keys, so you're gonna add some sort of, like, a networking proxy layer, like credentials proxy, right? So people are trying to solve that, like, what the agent stack look like, the agent cloud for the background agents. And then the other question is, how do you convince IT to bring differential access? Like, who, when, like, depending on which environment the agent is running, or, for instance, which Slack channel, like, what kind of credentials you want to bring to the agent, right? All those problems are still unsolved, like, we're very early, as I said, the models are way there, but the products that are really using these capabilities are still lagging behind, so a lot of opportunity right now.

• 12:58Auren Hoffman?

  Yeah, and it's one thing of, like, you're, like, some people listening to this, they're like individual creators, and then you can kind of move fast, but once you start to become a part of a larger organization, like, the complexity goes up exponentially right now.

• 13:15Speaker 00

  That's right. The individuals are where, like, we're seeing the biggest, like, the AI-pilled audience, like, top committers and all, and the enterprises feel, like, constrained on a couple of things. One is, like, the token budgets, like, because most of these tools are successful locally. One of the things they don't have is auditability, are these tokens being spent on their own projects, or are people doing their own side projects? You don't have any idea, right?

• 13:39Auren Hoffman?

  Oh, interesting. I didn't realize that, okay.

• 13:42Speaker 00

  Like, yeah, I mean, for a startup like us, it's okay, like, I mean, but for larger companies, they have to think about if some developers are spending, like, five thousand bucks a month.

• 13:49Auren Hoffman?

  Right, what are they spending it on, right?

• 13:51Speaker 00

  I've even seen the impact, or are they just doing some side projects? And, because it's so easy to do, to do, build anything now, right? You could be building your own

• 13:58Auren Hoffman?

  Yeah, good point.

• 13:59Speaker 00

  Home automation system, for God's sake, like, anything, right?

• 14:01Auren Hoffman?

  Totally, which, which we've all done, right?

• 14:03Speaker 00

  So, yeah, so, so that's, that's the biggest thing right now, like, a lot of the success has been, like, with the prosumer audience, like, lot of individuals and running head with, like, this hundred, two hundred, or other plans. But the enterprise is still, like, the problem has not been fully solved in terms of the cost visibility, and how do I operationalize agents in terms of security? So a lot of gaps are still there. That's why the background agents have been a hit and a miss. People have been trying those ideas for the last, so, more than a year now, but we haven't seen, like, big success in moving the token spend to cloud and the background agents.

• 14:40Auren Hoffman?

  Okay, there's like code review, and then there's like, security review, security vulnerabilities. How related are they?

• 14:49Speaker 00

  I would say there are two sides of the same coin. Like, during code review, you are looking at your best practices, your whatever guardrails you have, and not, and of course, security as well, you want to understand whether you are going to have P I I leakage, because these agents do make a lot of mistakes that do compromise the security posture, right? When we look at security review, like, you have had these, like, tools of the past around SCA and S-bomb tools, lot of, like, Sply Chain Analysis, lot of, like, dynamic tools as well, static analyzer like SamGrep and all. So those are getting supplanted with now a lot of AI-based analysis, so you could really reduce the false positives, because that's been one of the biggest complaints with the

• 15:33Speaker 00

  snakes and SamGrep of the world is, like, a lot of false positives. So people have, I know, thousands of findings in a mature repository, but they don't know what to prioritize or which ones are even real threats. Now with AI, some of that has been accelerated in doing a security review, and it's also, like, almost like an arms race, which is very interesting. Now you're seeing really capable models are able to also find vulnerabilities, right? That's why the government's trying to regulate some of these, like, Methos class, and the bigger models, because they're so good in, in finding zero day problems. At the same time, your review has to catch up, it's like an arms race, I mean, you, you will have to fight fire with fire, you have to do, do a security review with another AI.

• 16:15Speaker 00

  So it's a pretty exciting time, I think, reviews and security reviews are really, pretty much, both sides of the same coin. It's really about the latency at, at this point, like, yes, we want, like, a fast review on a PR that can finish in ten minutes, but if it's an important feature, maybe you want to run a thorough security review that can take, I don't know, half an hour. It's just about spending time and more tokens and making sure that there, like, no threats that can get in.

• 16:40Auren Hoffman?

  And there's many different types of reviews, because, like, you could even have, like, an agent, like, you, like, if they're using the product, right, and trying to do prompt injection, or trying to do other types of things to really try to jailbreak the product as well.

• 16:55Speaker 00

  Yeah, that's right. I mean, the, if you're building an AI product, securing them is also, like, challenging, given how non-deterministic those systems are. So you could casually an agent, talk to it nicely, and it will give you, spill all its secrets, including, I know, its system prompt, or whatever credentials you put in, that's why it's not safe to add your credentials, and people have had so much problem with open claw, for example, right? I mean, it's great as in hobbies, but if you're, like, really a serious enterprise, you have to take those concerns very seriously around guardrails and, I think we're, like, in very early, I mean, a lot of the products of the future are going to focus on, how do you, like, pick up the agent's reasoning traces and do analyses on them?

• 17:37Speaker 00

  Like, did these agents, did they, did these sessions got compromised in some way? So there's going to be, like, a lot of new startups, which will be tackling, say, especially the agentic security, like, it's not about, like, a single prompt anymore, it's about the trajectory now, like, even the evals and security looks very different now in the new world.

• 17:58Auren Hoffman?

  What can a human do in code review that we cannot do with an agent yet?

• 18:05Speaker 00

  Right, so it's very interesting, right? I mean, if you look at the code reviews, they have certainly evolved than how they were two years back. So the type of mistakes an AI agent makes is not the same type of mistake a human makes, right? So you're not looking at issues which are simple, try, catch, misses, or some error handling is not there, like, the code is better than what most humans will write, right? If you look at that code in isolation, now, if you zoom out and look at the bigger picture, I mean, is this pull request something that is, should be merged or not merged, which is, like, is it adding to the entropy of the code base? Is it really using the right patterns?

• 18:46Speaker 00

  Is it, because in isolation, it looks perfect, but half the time, I mean, you will see that it's like leaving some shadow parts behind, it's like changing the code flows of your product and so on.

• 18:56Auren Hoffman?

  This chain that you're adding could make things slower, or could add, you know, your memory leaks and all these other issues that could happen, right?

• 19:03Speaker 00

  Right, because when agents are generating code, they have a very singular focus, taking the user's prompt and going all the way, and depending on how assertively you prompt the agent, they will just go out and do the thing, even whether it's the right thing or not. Now, as an enterprise, as a company, like, I mean, you have a team of developers, some of them are good with prompting, so they don't, like, push the agent so much, but sometimes you will, like, force the agent to do something so unnatural, that you have to detect that in the adversarial review against whatever your hard invariants are and so on, are they getting violated, right? So the nature of review is definitely of human review, is moving away from line by line of code reviews, because that's where AI is already doing a better job than humans, to higher order reviews.

• 19:45Speaker 00

  So you are looking at higher abstractions in understanding the architecture, understanding the other sort, blast radius impact of this change and so on. Yeah, one thing is sure, the review is not gonna go away, like, I mean, the guardrails never go away. For example, like, Data Dog is one example, your cloud platforms are becoming better and better with time, your Kubernetes makes sure you don't crash, but doesn't mean you don't, you switch off your monitoring, right? So, so the review is gonna evolve, but it's never gonna go away.

• 20:11Auren Hoffman?

  Now Cursor bought a code review company, but for whatever reason, that hasn't like really taken off. Like, why, why, why has that not taken off?

• 20:21Speaker 00

  That's a good question. So, so yeah, I mean, they exited because also like because CodeRabbit was like still like a better product in many ways. And also that company started pre-gen AI and with a different philosophy that if you look at the time before pre-gen AI, humans were still reviewing, but the review was still a bottleneck. It's not like, it wasn't a bottleneck.

• 20:41Auren Hoffman?

  Yeah, it's been a bottleneck my whole life.

• 20:44Speaker 00

  Exactly.

• 20:45Auren Hoffman?

  Sometimes you're waiting and you're like, I wanna, I wanna push this and you're like, you're, you've been, you're waiting for your colleague to take a look at it, but your colleague's on vacation or something or, you know, whatever it might be.

• 20:55Speaker 00

  Right. So for that company, like the focus has been around graphite, right? So the focus had been around stack pull requests. So the idea is like, if the humans were in the review path, they can only review if the changes are small and bite sized. So the idea is like you checkpoint your big feature into smaller pull request or stack of commits that you can review one at a time and then stream all of them together, right? And then the owners is on the author of a pull request to actually checkpoint the commits that way, to create a stack which is meaningful enough for the reviewer to understand. For instance, the first commit of a stack is you made a schema change. The second commit could be you made some business logic around it, then you add a API route. So you can actually stack your changes as you're building software. But that was like

• 21:40Speaker 00

  the idea like pre-gen AI, right? Now with AI, you can't really, you can ask AI to stack, but usually makes no sense. Like you can just ask it to go build a feature and let the reviewer figure out the right way of reviewing these changes. So that's where the disruption happened, like the techniques and the tools that we had designed around managing code changes before gen AI look very different post-gen AI.

• 22:03Auren Hoffman?

  You think like, maybe there's a way of like integrating the code review into your, wherever you're storing your repo, GitHub or whatever it might be, like you would think like somehow like the closer the integration there would be better. But for whatever reason, like GitHub doesn't really have a good solution either. Like, how do, how do you see like the world evolving when it comes to code review?

• 22:30Speaker 00

  Right. I mean, this is a space which is a big addressable market. So platforms like GitHub will always compete with every other player in the market as well. I mean, it's almost like how Amazon was AWS, right? They had cloud watch, but then still Datadog became very successful. So the way we see this market is there is going to be best of breed products like CodeRabbit, which will, because a lot of companies care about best of breed. Like if there's some CFOs, they'll say, okay, we, we anyways have a big contract with Microsoft, let's just buy everything from one place. But there are a lot of companies that go and buy best of breed solutions like they'll buy Zoom, they'll buy Slack, right? And they're buying CodeRabbit, right?

• 23:10Auren Hoffman?

  Yep.

• 23:11Speaker 00

  So, so that, that's what we're seeing. I think this is one of those times where a lot of the VCs are also a little bit anxious about model labs going and winning everything, where we see kind of the world in a different way. We see there's a huge opportunity to solve problems with custom harnesses. Like code review is one such use case where CodeRabbit has built a custom harness, which is like, gives a best price to performance right now than anyone else in the industry. And that resonates very well. And that's why our like growth has been pretty much unobstructed despite GitHub competing with us over the last two years and so on.

• 23:47Auren Hoffman?

  You have a very interesting lens because you're seeing code across lots of organizations and helping fix code across lots of organizations. Is there some kind of like, in some ways, like a co-op that you can, that could happen where, okay, if you can fix one in one organization, that can help you fix another organization? Like, does that somehow make your model better? Or is it just so proprietary that you can't actually use that data to the next one?

• 24:19Speaker 00

  Yeah, some of the learnings across orgs do carry forward. For instance, with the reason why we have been further ahead of everyone is because we have a large install base in open source. And we, every few seconds, there's a pull request getting reviewed in open source. In fact, yesterday I was looking at like Kubernetes community sent out a newsletter saying that they love CodeRabbit, right? So we are in like very popular mission critical repositories right now. And that's like actually have helped acting as like a feedback loop.

• 24:50Auren Hoffman?

  Yeah, that's great. Cause you can, you can use that. You're allowed to use it and you don't have to, it's not proprietary. Yeah, okay.

• 24:55Speaker 00

  That's right. We're in a very unique position compared to a lot of the AI products out there, which work in a more private setting. So you can't really train on their data. In our case, we have been taking all the learnings we have from those runs, which are all in the sandbox. There is agent trajectories involved and taking it back into improving the harness, improving the context assembly, and even like choosing the right models that we want to use. Like we have so much data coming in that we are able to pick the right cocktail. So we're not a product where we let the user pick a model from a dropdown. We, we come up with the right cocktail of agent and sub agents and different other models, right? And that's proved very valuable. So we're taking the learnings across these projects and helping everyone else out there. At the same time, the

• 25:38Speaker 00

  product also builds proprietary learnings within an organization. So there's still like a lot of tribal knowledge that does not translate well across organizations, because sometimes people do things which are counterintuitive. I mean, the AI agent will say, okay, this is the best practice, but then someone will go and correct CodeRabbit. No, this is not how we do things. We are not your average data set. We are different, right? And CodeRabbit will remember that fact and then fine tune the reviews based on those learnings for that organization.

• 26:07Auren Hoffman?

  Okay. So when, when we met you roughly two years ago, when you did your seed, it was clear, like you had a cool product. You guys were like super technical. You're like really into the, you're into the product. What we didn't know then is also you guys are like masters of go to market in marketing as well. Like walk me through like the marketing playbook that you guys have used over the last couple of years.

• 26:34Speaker 00

  This is my third startup, first of all. So, and go to market across all my startups has looked very different. Like my first startup was mostly in the cloud infrastructure space. So right when Kubernetes was taking off. So the GTM there was very content focused, like getting into hacker news led to, and led to a lot of like signups and meetings. We're mostly SDR focused in my first startup. This one has been all word of mouth, product led growth company, right? And we kind of ground up, build that intentionally because this is one of those markets where we had seen previous successes with Snick, for instance, is one company we took a leaf out of their playbook a little bit by making the product free for open source. And there was

• 27:19Speaker 00

  another reason. When we started back in 2023, those were very early days of AI. And a lot of people didn't even have this new habit of using AI, right? So we wanted to make sure that light as many fires as we can, like get the product out there so that people build this new habit, right? That was the biggest battleground, more than pricing and other things. It was like, would people accept or reject AI in, in pull request workflow, which is very serious, right? So one of the strategies we did there was to open up the product for open source for a variety of reasons. One is wanted to show people that how effective this product is, like seeing is believing. So those kind of products, you can't just put a message on website and make promises. You have

• 28:03Speaker 00

  to really show people, right? And open source was the best showcase we could find. Second is open source maintainers actually had a pain point. I mean, there were a lot of like contributions from unknown people. And this was like the thankless job. So they were already struggling with code reviews. And thirdly, we knew that the open source adoption is going to set the flywheel for constant product improvement. So we kind of like hit two, three birds with one stone.

• 28:28Auren Hoffman?

  Right. You're making your, your, your marketing and you're making your product better all in one big thing.

• 28:32Speaker 00

  That's right. And these open source projects were like, almost like influencers. These maintainers are influencers. They have a lot of weight in, on Twitter and other places. And so, so one door led to another and we got like viral adoption in open source. We also had a lot of viral adoption in the early days in Japan, very interestingly. So we had no boots on ground, but we had, I don't know, 30 blog posted by end users about how good the solution is.

• 28:55Auren Hoffman?

  Yeah, we noticed that too. Like why, why did the Japan thing take off the way it did?

• 28:59Speaker 00

  That we will never find out. Like, I mean, it just got viral independently. We had no hand in that. It just went viral on its own. While it also went viral in the US, thanks to open source. And it was also like, I think the time in the market, like we were early movers. It was, AI was a novelty. So a lot of people were curious. At the same time, the product lived up to its promise. It wasn't just slop. It was actually adding real value. So, so a lot of things came together. And even on the marketing, we were like very influencer focused. So we would work with top content creators to talk about CodeRabbit. We had like, for example, fireship video placements and all. So that always led to like signups and awareness. We have billboards, like you have

• 29:41Speaker 00

  seen SF, like we have like, we did the station takeovers. So all those things helped a lot.

• 29:46Auren Hoffman?

  I was in the Amtrak station in New York, taking a train to DC, and the whole station was, was wrapped in CodeRabbit. And I'm like, wait, why, why, why it like, are there even that many developers? Like, what was your reasoning to do something like that?

• 30:03Speaker 00

  Yeah, it's for the mindshare. I think we just wanted to be in the series where we think there's like developer population. We did one in Austin, I believe we did. In Bangalore, we are running ads. We're running in the Bay area, of course, South Bay SF. And we know that the pain point is so prevalent. Like this is a prosumer product. So if you look at our customer base, it's millions of developers that we want to reach out to.

• 30:24Auren Hoffman?

  Yeah, you even have a free process. Starts, you know, you can have a freemium version and then people like upgrade to a little bit more, then you can upgrade to a little bit more. And then all of a sudden, like the whole organization starts using it. Is that, that kind of typically the way it works? Like, like a one developer starts using it, they tell another one inside, they tell a few more, then you've got a, a pot of 10 and then, and then it kind of spreads and then eventually you do like a enterprise deal?

• 30:48Speaker 00

  That's right. It was always about getting people hooked into this new habit. So a lot of times we get pulled into this larger deals because some developers in that company have tried CodeRabbit in their own personal repositories or some open source project that they care about or they contribute to. And that's usually like how we get in, like we don't have any very strong outbound operation. It's all inbound for us. And that's, that's a primary driver. If when we do a survey on where did you hear about us? Majority would be through the developers in that company who tried out in open source or something. And so this is a very effective GTM motion. I don't know how much it translates to other domains, but for dev tooling, this has worked really, really

• 31:27Auren Hoffman?

  You have like very, very big companies that are already adopting, like some of the more iconic companies are already adopting CodeRabbit. It's interesting to go from like this prosumer to a very enterprise-y thing where I'm sure you need lots of systems and you need to review things and admin privileges and like, how does one decide how to build the products for this like wider enterprise?

• 31:57Speaker 00

  It's a very interesting challenge and you know, the product does get complicated as you add more knobs to it, which at the same time become too overwhelming for the prosumer audience.

• 32:08Auren Hoffman?

  Yeah.

• 32:08Speaker 00

  They like simplicity and striking that right balance has always been like a big debate. I mean, of course you have to build the go-to-market motion first, right? I mean, so for the longest time, our revenue had been mostly PLG self-serve revenue. And since then, we've started building a very successful sales engineering organization, a lot of like enterprise EEs and further segmenting them like mid-mark, mid-enterprise, strategic, like we've done all the segmentation, right? Then the other thing is a product, right? The product has to be enterprise-ready and we have customers who want self-hosted and they want us to be in Europe, like whatever the GDPR reasons, right? And so you have to do all that, like now with the agents, it's also so easy

• 32:52Speaker 00

  to build all those configurability that people need. The real trick is in the product layer, like how do you keep the product simple, but also like still programmable enough that the enterprise is even on the legacy code bases with different velocities of shipping can still tailor it, right? So yeah, I mean, everything has to go hand in hand.

• 33:11Auren Hoffman?

  I mean, going like essentially from zero to 50-plus million in two years is kind of insane though still, right? Like, and like even just in an organization, you're hiring all these people, like how do you, yeah, maybe you're not hiring as many people as like a company would have done 10 years ago if they're growing that fast, but you still have a lot of people you have to absorb and train. Like, how do you think about just generally growing a company in that kind of hyper-growth mode?

• 33:38Speaker 00

  No, that's okay. Like, we have 250 people, so it's not like the AI has eliminated.

• 33:42Auren Hoffman?

  Yeah, that's a lot. That's a lot of people and you gotta train them, you gotta motivate them, you have to like get them excited, you have to get them all on the same page, right? Just even like interviewing that many people, a lot of people.

• 33:55Speaker 00

  Yeah, that's why you have to scale with like a professional leadership, like, and the needs change as you went from let's say zero to 1 million, 1 to 10 was a different need and then 10 to 50 has been a different need. Like, so some of those needers, either they up-level themselves or we had to bring in other leaders on top, right? And tap into their network to hire more A players and so on. And that's what we have done with the sales team and product teams and the throne. Like, but on the engineering side, I would say we have not grown that much. Now, it's very interesting that we are living in a time where our shipping velocity has exceeded our ability to market those products. This is the first time I'm seeing that. Usually you will have marketing run ahead, promise the features which are like a quarter or two away and it's flipped.

• 34:40Speaker 00

  Now, we have already shipped the changes and marketing is trying to figure out what happened, right?

• 34:46Auren Hoffman?

  It's funny, yeah, you're usually selling like three quarters ahead and now you're selling like one or two quarters behind.

• 34:52Speaker 00

  That's the reality, very interesting and not just us, even the model labs, they're shipping, building in public, broadcasting live, so the marketing, nature of marketing has changed, right? And you don't need like a big engineering organization to keep your go-to-market team busy. You need a lot of sales because you still need to have those meetings, you can automate with AI, but engineering and marketing to some extent, content, we are seeing rapid, the shipping velocity thanks to AI, but on the ground, you still need sellers, you need that DNA. You have to still sell the value, the ROI, the why to the end user and customers, especially the leadership in the enterprise teams where there are more stakeholders involved and get those deals to the finish line, right?

• 35:35Speaker 00

  The POC and the bottom swell is never enough. Like, you have to find the economic buyer and convince them as well, right?

• 35:43Auren Hoffman?

  Now, there's this consensus or some people believe AI is gonna wipe out or significantly reduce the junior engineers and I've heard you say it's kind of closer to the opposite, that new grads can do now in two hours where it used to take two weeks. Like, what does the entry-level job look like in a few years?

• 36:04Speaker 00

  We still need engineers of all kind of calibers, like the senior engineers definitely bring in experience and system-level knowledge and it's about like the pod that you want to create when you're delivering software. So we have started like organizing our teams into smaller, these units called pods, where you have someone in the driving seat with an agent, but you have the UX team in the same room, you have the product team, they're all touching the code by the way, but they're on PCs, right? So interns, we also hire a lot of interns and we are able to make them successful. In fact, what I love about interns is they are just limited by imagination at this time, like whereas some of the engineers will still try to go a little bit deeper into the code, not really to let go, try to like do more tactical driving when you could now take a step back

• 36:46Speaker 00

  and let the agent also make a few decisions and run faster. So we're seeing that imbalance where sometimes even the senior people are being held back because they're too afraid to let go.

• 36:56Auren Hoffman?

  Yeah, yeah.

• 36:58Speaker 00

  And at the same time, you also need their experience to watch the backs on what to review and where to, how to validate, how to scale, because it's easy to prototype, but some of these features, once they go out, you still have to solve the scaling problems, right, which you only discover in production. Yeah, so you need like people who are great with imagination and curiosity. You need engineers who understand distributed systems, scalability, you need product, UX, everyone to come together and touch code at this point. So everyone is coding in a way, just their own PCs.

• 37:28Auren Hoffman?

  Now, when I started my career writing in Java, then maybe in Scala or something, I always read code, but I never in my life have ever read assembly or certainly never read machine-level code. Are we going to move to a world where nobody reads code, just like when I started a career, no one read assembly?

• 37:50Speaker 00

  I think we are already almost there, like even I'm mostly using my agent or CodeRabbit to ask the right questions about the code, because as an architect level, like I usually, just with a few questions, I'll have an idea that this is going in the right direction or not, right? It's just about talking to the agent now more than writing the code. And that world is already there and a lot of this is going to be codified in tools like CodeRabbit, so you don't need an expert person to do all this in every company, so the tools can do it for you. But yeah, I mean, I think that time is just around the corner when you are really not reading the code. It's like taking the steering wheel of the car, we are almost at that point.

• 38:28Auren Hoffman?

  Yeah.

• 38:28Speaker 00

  And we have already seen that IDEs are not future-proof, right? I mean, the first time I have moved out of my terminal, I used to be a new VIM person for the last 30 years and I've not been in my terminal in the last six months, I'm in Codex desktop app.

• 38:44Auren Hoffman?

  Yeah, yeah, yeah, which is also beautiful and easy to use and kind of fun, right?

• 38:50Speaker 00

  Yeah, and just talking to the agent, right? And you need tools like CodeRabbit like to do an adversarial review, so I have a loop set up, so until CodeRabbit is happy, we just keep committing, right? And then once everything is great and if the feature is not high-risk, you don't need to involve another human, you can just ship it, right? So we've started getting a confidence that even an agent take approval is good enough for 80% of your features.

• 39:17Auren Hoffman?

  There's one, okay, there's a review, there's a code review. There are, a lot of other people are using, whether it's Codex or Cloudera, to create content. So I've got like, I've got long-running Codex tasks, which are essentially creating content for me, doing lots of, in some ways, that's kind of like code and I could use a review system to review my content, just like people review their code in a way. Like, could you imagine there's like a, like analogy to review content?

• 39:54Speaker 00

  Yeah, it's a very interesting thing, like any problem today that can be translated to a coding problem, you're gonna move 10x faster. So if you can run your marketing as code.

• 40:04Auren Hoffman?

  Yeah.

• 40:04Speaker 00

  I mean, that is like, can you run your company as code, right?

• 40:06Auren Hoffman?

  Yeah, exactly.

• 40:07Speaker 00

  And if you can eval against something, it can be your design test or something, the agent can loop around and get to really a picture-perfect, one pager, slide deck, whatever it is, right? And we are also, we've been moving in that direction. In fact, I have Codex sessions running, doing sales deck, one pagers, right? And Markdown and Marp, so I use this Marp as one of the tools where it creates slides, right? So the idea there is like the humans still need to understand these changes. So if these agents are gonna run longer and longer periods of time, there's gonna be a new concept of explainability that's going to emerge, just like how DataRock did observability, there was a lot of chaos in fast-moving environment, just like that. There is going to be a lot of chaos.

• 40:50Speaker 00

  Agents are gonna create massive amounts of work and you're gonna be bottlenecked on reviewing and understanding those outputs.

• 40:58Auren Hoffman?

  Yeah.

• 40:58Speaker 00

  Like, whether it's content or even information retrieval, you want to like build trust in the output is like, did this agent even go and look at the right data? If you are asking it to get your, I don't know, weekly website visitors, did it even access the right tool, right? You want to build trust, right? And I think there's going to be like, as I said, a lot of products that are gonna take agents, reasoning traces or reverse engineers from the output, just like how we do in review, we kind of reverse engineer what could have happened to build sort of an explainability walkthrough. And that's what the humans are gonna review. Whether you're doing content generation, because everything is code. Code is now the execution layer for these agents, right? And so you need reviews, not just for your software, but also for content now, because everything is code end of the day.

• 41:41Auren Hoffman?

  That's exactly what I did a long running Codex task. It took about five days. It created all this content for me. But honestly, I, in the end, like, I didn't really understand what happened. I'm like, wait, what, what did you do? Like, I've been, I'm trying to, like, it was very, very hard for me to really get a handle on what it did. And what was the level? I could like go in and inspect some things, but it was like, almost like a very, it was almost like a code review, like a human code review that I had to go in and do. When will something like CodeRabbit be able to do that for me? Or couldn't it already do it? I just don't understand it.

• 42:17Speaker 00

  I mean, with the agents, you could do it. Like, the thing is that for us, our audience primarily has been the developer audience, right? And we have been like bringing some of the products into other surfaces, given how much, how many installs we have in GitHub. So it's kind of natural for us to go from GitHub into Slack and other systems like Jira Linear. Now, Jira Linear are losing importance, interestingly, but Slack is becoming even more important, is what we're saying.

• 42:41Auren Hoffman?

  Yes, for sure, yeah.

• 42:42Speaker 00

  Right? And those are the surfaces where we are trying to bring general purpose agents, which is building on the code context, but then you can use them to other work. And one of the things we are doing is instrumenting those agents. So one of the cool things we have done is like, because we control the agent, it's a background agent. We are picking up, I know, tens of megabytes of reasoning traces out of these runs and running layers of AI on top to review those, right? And we're building visualizations to understand not just your coding outputs, but knowledge work as well. So building a more generalized explainability layer that goes beyond code reviews into any kind of agentic run visualization. And we're very bullish about the future.

• 43:24Speaker 00

  I think the explainability is going to be even a bigger category than what observability was in the past. And it's gonna start with code review, because that's been the first biggest BMF areas of agents. But very soon, people are gonna realize that they have to run marketing as code now.

• 43:41Auren Hoffman?

  Accounting as code, legal as code, and we need to explain all these changes all the way through.

• 43:47Speaker 00

  Right. Just like how you did infrastructure as code, you're gonna start committing your marketing assets in GitHub.

• 43:52Auren Hoffman?

  Yeah.

• 43:52Speaker 00

  So it's all gonna end up in GitHub eventually, right?

• 43:54Auren Hoffman?

  Yeah, yeah. And by the way, be very helpful. I mean, like, if you, if you have a legal agreement, it'd be very helpful to have, uh, you know, be able to go back in time and understand like your commits to your legal agreement and how those changed over time and who committed what. And in a legal agreement, you may be writing a contract with your customer and you most might be committing to it. And you have to resolve these kind of commits in some sort of way as well.

• 44:21Speaker 00

  Yeah, that's right. The world we are going is like, I don't, like, a lot of people on Twitter, some thought leaders will say, okay, the PR is dead. Like, you don't need pull requests. But we disagree. I think it's going to become the center of gravity in this entire movement in many, many ways. Because there's no choke point. You can't just ship product without taking a look first, and especially if you are in a team setting, right? So this central quality gate is going to try, go beyond even software reviews that we are doing right now to other things. And yeah, I mean, I think the review step is going to be the most important step. That's what humans are gonna end up doing, just doing the work, right? And maybe upstream, giving some taste and collaborating on the taste as well. So it's either like you're collaborating on what to build and reviewing the, the prompts that the agents will run, or you're gonna end up reviewing the end output.

• 45:08Auren Hoffman?

  It's interesting, there's so many people I know who have never coded before, just, you know, even a few months ago. They're now, like, they're now using things like, they, they didn't even have a GitHub account. Now they have a GitHub account. They have all these kind of things that are, so you, you can start to see these like other tools that are out there really explode. It seems like the Microsoft acquisition of GitHub, which was, I don't know, seven years ago or ish, seems to be a very good, like, there seems to be a lot of these like tools that are in, that will be enduring going forward.

• 45:40Speaker 00

  Yeah, that's right. I mean, I think GitHub was a great pick by Microsoft a few years back. No one saw it coming, that how important this is going to become to the agent economy, right, going forward. And of course, people are trying to build serverless Git. I mean, Cursor is launching a competitor, so are like a few players. But it's almost going to be like very hard, like, it's kind of the blue sky moment. It's, once you have the ecosystem, the tooling around it, the open source is all in GitHub. So it's gonna be very hard for another platform to emerge.

• 46:09Auren Hoffman?

  And you want your, like, your repo to be independent of your, you, you like, because if you move from Cursor to something else, you don't wanna all of a sudden like have to, like, you, you want it to almost be an independent system.

• 46:22Speaker 00

  That's right, right. And you want your reviewers to be independent. A lot of people buy CodeRabbit because of that reason, is if you were to use your coding agents for review, their context is already polluted. They're like too agreeable, like, once you have a session.

• 46:35Auren Hoffman?

  Yeah, yeah.

• 46:35Speaker 00

  It wouldn't question its own judgment and decisions, right? So you need a very adversarial clean session, which is programmed in a different way with different guidelines. And also, it can be very thorough. You don't want to do that inline on the developer laptop, like, you want to do that in a pull request model, in a background agent that can run for fifteen, twenty minutes and be very thorough before some things go out.

• 46:58Auren Hoffman?

  Now, a couple of personal questions. Okay, Flex Capital, we're, we're very happy investors in CodeRabbit. We're also investors in Tracer, which is run by your brother Tanvir. Okay, what, what did your parents do well? Because obviously, like, they did something well. So what did they do well? How are you guys raised?

• 47:20Speaker 00

  No, we've just been like very entrepreneurial, like, we started the first company together that was back in 2014 called Nextel. And then we did second company together called Flux Ninja. But by the way, we didn't do very well. We ended up, CodeRabbit kind of came out of that startup as well.

• 47:35Auren Hoffman?

  Yep.

• 47:36Speaker 00

  So yeah, so me and my brother have been like very close.

• 47:38Auren Hoffman?

  Was there something your parents did that got you entrepreneurial?

• 47:43Speaker 00

  It's about risk taking, right? So the, one of the things that parents can issue is to either be like risk averse and maybe get a job somewhere. Like, that's what most people say, go get an MBA and getting hired by maybe Google is seen like, like ten years, fifteen years back, everything. That was the thing, like, you were going into a great brand name company, starting your career, whether it's consulting or something, right? But my parents were more like, just, they were like, they always like very much risk focused. Like, I mean, you hire the risk, the higher the reward. And that's the game we learned to play growing up, like, whether it was the schooling that we got, even going to the schools that were more focused on leadership and those qualities, right? To even the culture at home, it was about what's, what's gonna happen? Like, you always have something to fall back to.

• 48:27Auren Hoffman?

  Yeah.

• 48:28Speaker 00

  So we could be like pretty much unshackled and take risk early days in our career. And even now, we take risk, it's just like different appetite. So I think that would be the biggest thing that I would say we did growing up.

• 48:40Auren Hoffman?

  Now, if you're gonna give advice to an eighteen year old for their career, you know, it's uncertain time. A lot of eighteen year olds are not sure what they should be doing. Like, what advice would you be giving?

• 48:51Speaker 00

  Right now, I would say is the best time I've seen in the history to do startups and create value, right? These inflection points are very, very rare, like, it's a once in a decade or two decades kind of an opportunity. And there is just so much, like, I would say, land grab opportunity in terms of the use cases. We are like in the very early innings of AI. Like, what we are building is not even one percent of what these models even today are capable of, right? So you're limited by imagination, like, right now is a time, if you are like not financial, there's no financial reason to join, like, or pay off the loans. Go out and take a risk, right? And a lot of VCs in the Bay Area, like, I mean, this place, this place is very special

• 49:35Speaker 00

  in terms of even how much people are willing to bet on the young talent. Like, if you look at the Cursor founders, Cognition, they're all like young talent, like, on the younger side.

• 49:45Auren Hoffman?

  Yeah.

• 49:45Speaker 00

  Because even VCs have realized that they are the ones which are moving fast with AI compared to a lot of the experienced folks who are now still like hesitant on adopting and questioning the priors, right? So I think it's a great time for like eighteen year olds, like, first of all, learn how to use these agents really effectively. Even a single person can now be effective as, I know, twenty, thirty people, right, very easily, even if you're doing it in the right way.

• 50:10Auren Hoffman?

  How are you personally using AI, like, in your personal life?

• 50:15Speaker 00

  So in my personal life, I mean, beyond coding, I mean, I'm trying to use Codex more than ChatGPT, I would say, given that code is now the foundation of everything. So, so that's become my go-to tool, like, nine months back, I used to go to ChatGPT, use sometimes a Pro model as well, especially when I'm doing some deeper research into planning something in my house or something. But now it's becoming more and more code oriented, right?

• 50:41Auren Hoffman?

  Yeah, I found like even planning a vacation using Codex is better than planning a vacation using ChatGPT.

• 50:50Speaker 00

  That's right, that's right. We're seeing like the coding agents are going to be maybe the main substrate now, the underlying platform powering all, all kind of work or non-work, I'm coding or non-coding kind of use cases, that I'm already seeing in my personal life. I mean, unfortunately, I don't get much time for personal stuff.

• 51:07Auren Hoffman?

  Yeah, of course, yeah.

• 51:08Speaker 00

  My wife's been meaning to like having my own, like, I've got two daughters, like, to teach them coding at some point, especially with Codex. Not even finding time. But overall, yeah, I mean, in terms of AI, I use Grok a lot in my Tesla, so.

• 51:21Auren Hoffman?

  Yeah.

• 51:21Speaker 00

  I've got Grok set up, so it's so convenient, like, on the way we, just to keep the kids busy, we'll just talk to Grok.

• 51:28Auren Hoffman?

  And, and you just, just for fun, you ask questions or keep it going or?

• 51:31Speaker 00

  Like, it's really good with grains, like, you wanna like do Mad Libs or something. It's fun, like, try, try those things, it's, it's cool in the car, yeah.

• 51:38Auren Hoffman?

  Yeah. All right, this is amazing. Last question we ask all of our guests. What conventional wisdom or advice do you think is generally bad advice?

• 51:48Speaker 00

  So I mean, all kind of advice can be good or bad advice. It all depends on the situation. Then you always see like, the main thing is like, in terms of, a lot of the good advice that used to be good, like few years back is now bad advice, is I would say. Like, a lot of the things have changed under everyone's feet on how to build businesses, on how to think about funding, on what makes someone successful, not successful. Everything has changed. So I would say a lot of the advice that even comes from VC, like one of the examples is like, few years back, you will start in AI, any kind of company, you will go after some of the marquee logos. You will go say, okay, I want to get Uber as my customer, because if they are buying your solution, because everyone looks up to them.

• 52:31Auren Hoffman?

  It's a social proof or something.

• 52:33Speaker 00

  Yeah, it's almost like the crossing the chasm curve. You have seen that. So you have the early adopters and so on. Now, VC still does that. They'll go and tell their entire batch to sell to these marquee logos, they're going to fool around, spend time. Interestingly, that curve has now inverted. It's an inverted crossing the chasm. In fact, the companies that are benefiting the most from AI are actually more mature companies. In fact, they are one of the first ones to adopt, because they benefit a lot more. They have a lot of average talent.

• 53:04Auren Hoffman?

  Yeah.

• 53:04Speaker 00

  The models are making them move faster compared to, even like, a lot of people at companies which are like Bay Area startups, they already had good engineers. So initially, they didn't see enough value, because they were anyway smarter agents, smarter engineers than the AI models were, right?

• 53:24Auren Hoffman?

  Yeah, yeah, that's right.

• 53:25Speaker 00

  So now maybe that has inverted. Now they are adopting agents as well, but at least 12 months back, that was not the case.

• 53:30Auren Hoffman?

  Right, like the average engineer at, I don't know, Nike, maybe Nike was never able to hire like the most talented engineers, and so they really needed these tools really quickly.

• 53:41Speaker 00

  Right, because what AI is doing is like taking the best practices it has learned to an average person. So it may not, like, I mean, for example, I can do marketing, I may not be the best marketer, but it does level the playing field for me more than someone who's already like a top marketer. They may not see enough value with these agents, right? So, so the, the curve has inverted, and that's a battle wise everyone still like living by that, when you go to VCs and you want to like look at the quality of the logos, but none of that holds true in building a real business. Code Rabbit is one example. Now we have great logos, but to be honest, in year back when you guys came in, you will remember like half our customers were unknown, I don't know, Japanese companies.

• 54:18Auren Hoffman?

  Yeah.

• 54:18Speaker 00

  But the love was real, right? I mean.

• 54:20Auren Hoffman?

  Yeah, absolutely.

• 54:21Speaker 00

  So this is the first time I've seen it's an inverted curve, like companies you will usually reach out five years back at series C or D stage, we were closing them in series A or seed.

• 54:31Auren Hoffman?

  Yeah, this is super interesting. Thank you, Harder Deal, CEO of Code Rabbit for joining us on Summation. By the way, I follow you at Harder S Gill on X. I definitely encourage our listeners to engage with you there. This has been a ton of fun. Really appreciate you being on.

• 54:46Speaker 00

  Thank you, thanks for having me on.

• 54:48Auren Hoffman?

  One more thing before we go. I read a blog called Summation. It's the same as this podcast. The blog is about non-obvious idea sharing on business, talent, data, longevity, and random contrarian takes. If you like the conversations on this show, you'll probably like the blog. It's free, new content comes out twice a month. You could subscribe at orin.substack.com. That's orin.substack.com.