Podcasts about PMF

Ben Choi has spent three decades across the technology ecosystem—as a product leader, founder, and venture investor—and today serves as a senior leader at Next Legacy Partners, where he helps oversee $3.5B+ invested across premier venture capital firms and early-stage startups.In this episode of Investing in Integrity, our host Ross Overline and Ben navigate the intersection of venture capital, philanthropy, and moral leadership. Ben shares how Next Legacy's flagship model is designed to multiply capital—and then give it away.From there, the conversation goes deeper than mechanics. Ben outlines the values that shaped his leadership and why generosity is often driven not by one motivation, but by the shared joy of impact beyond yourself.Finally, Ross and Ben wrestle openly with capitalism—how it's the best economic system ever tested at scale, it can still evolve to be even better, and what responsibility future finance leaders carry to make that a reality.Whether you're a student trying to define success or a senior leader shaping institutions, this episode is a masterclass in using capital with clarity, humility, and purpose.Meet Ben ChoiBen Choi is a Managing Partner at Next Legacy. He manages $3.5B+ in investments with premier venture capital firms and directly into early-stage startups. His venture track record includes pre-PMF investments in Marketo (acquired for $4.75B) and CourseHero (last valued at $3.6B). He previously ran product for Adobe Creative Cloud offerings and founded CoffeeTable, raising venture financing before selling the company.Ben studied Computer Science at Harvard University and earned his MBA from Columbia Business School. He lives in Los Altos with his wife, Lydia, their three sons, and a ball python.

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Scott Stevenson is the Co-founder and CEO of Spellbook.Spellbook is an AI copilot for contract review and drafting, essentially “Cursor for lawyers.” They have 4,000 customers in 80 countries, and to my knowledge is the fastest growing AI company in Canada, and the largest company in the world built on a Microsoft Word plugin.Scott has been building in legal AI longer than almost anyone. We talk about why legal software was essentially untouched before LLM's, why the market is so hot right now, if it's sustainable, and how Spellbook navigates product differentiation compared to horizontal AI products like ChatGPT.We talk about why fine-tuning your own models was one of the biggest mistakes early AI companies made, how to build a network effect as a vertical AI product, and Spellbook's philosophy of “Don't sharpen your axe when the chainsaw is coming out tomorrow”.Spellbook spent a few years finding PMF before really taking off in 2022, and Scott shares their playbook for launching over 100 product experiments in three years, how to know when to lean in, and what it's been like scaling Spellbook post-PMF.Thank you to Numeral and Flex for supporting this episode.Try Numeral, the end-to-end platform for sales tax and compliance: https://www.numeral.comSign-up for Flex Elite with code TURNER, get $1,000: https://form.typeform.com/to/Rx9rTjFzTimestamps:(0:30) Spellbook: “Cursor for Contracts”(3:08) Building the world's largest Microsoft Word plugin(14:06) Why legal software was untouched before LLMs(18:32) $30 trillion moves through contracts annually(20:51) Why ChatGPT won't replace vertical tools(25:15) Fine-tuning was the biggest mistake in AI(30:00) Differences between pro and amateur gamers(37:38) Top-down vs. bottoms-up in legal AI(42:27) The long-tail of legal AI software(47:24) Building for models that don't exist yet(51:20) Skating where the puck is going(1:01:35) The legal bill that cost 50% of his bank account(1:09:33) Testing 100 landing pages in 3 years(1:14:06) The moment Spellbook hit PMF(1:19:17) Building new brands for each product experiment(1:23:10) Raising a Series B with a tweet(1:27:41) What Scott learned from Keith Rabois(1:31:16) Scott's favorite new AI toolReferencedSpellbook: https://www.spellbook.legal/Careers at Spellbook: https://www.spellbook.legal/careersPlaying to Win by David Sirlin: https://www.amazon.com/Playing-Win-becoming-David-Sirlin/dp/1413498817Find the Fast Moving Water by NFX: https://www.nfx.com/post/find-the-fast-moving-waterSpellbook's case study with Replit: https://replit.com/customers/spellbookTwin: https://twin.so/Follow ScottTwitter: https://x.com/scottastevensonLinkedIn: https://www.linkedin.com/in/scottasBlog: https://blog.scottstevenson.net/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Ahmad Sadeddin is the founder and CEO of Corgea. Corgea provides the security tools to find, triage, and fix insecure code. Ahmad shares:- Why you don't need to raise much to find PMF - stay lean: you should surprise people with how few people you are.- What is a small amount to raise? And what team size do you need? - Pivoting during YC and how Corgea found their first customers and the signs of Product Market Fit- The journey to Product Market Fit never stops- How Corgea worked towards Product Market FitThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:Ahmad Sadeddin https://www.linkedin.com/in/asadeddin/Corgea https://corgea.com/The Fatal Pinch by Paul Graham https://paulgraham.com/pinch.html

hema.to is building AI-powered diagnostic infrastructure for cytometry—a specialized area of laboratory medicine analyzing immune system data to detect blood cancers like leukemia and lymphoma. Unlike radiology or pathology where AI solutions are abundant, cytometry has remained largely untouched by the AI wave, creating both opportunity and isolation for the Munich-based company. In a recent episode of BUILDERS, we sat down with Karsten Miermans, CEO at hema.to GmbH, to discuss why they're deliberately keeping sales founder-led despite having paying customers, how South America became an unexpected beachhead market, and what it actually means to build infrastructure versus point solutions in healthcare. Topics Discussed:  From consulting project to venture-backed company: recognizing scalability in hindsight  The workflow integration problem killing healthcare AI implementations  Infrastructure versus technology: why healthcare AI isn't just about the algorithm  Learning ideal customer profile after 18 months of being "all over the place"  Why South America's governance structure enables faster adoption than the US  Resisting the urge to hire sales before achieving true repeatability  The 10-year vision: shifting from "watch and wait" to "predict and prevent" in immune disease GTM Lessons For B2B Founders: Pattern matching fails when you're an outsider—budget 18+ months to find your beachhead: Karsten assumed every application of their diagnostic method was the same and spent a year and a half "blue eyed" (naively optimistic) before identifying their true ICP. The outsider advantage lets you reimagine workflows insiders can't, but you'll incorrectly assume transferability across use cases. Don't expect repeatability in year one when entering regulated, workflow-dependent markets. Infrastructure requires multi-stakeholder orchestration—resource for enterprise complexity from day one: Karsten distinguishes technology (point solutions, single users) from infrastructure (shared resources requiring data exchange and workflow integration). In healthcare, this means integration into hospital systems, databases, and electronic health records across multiple stakeholders. "Every sale becomes enterprise sales" even for individual labs because of this infrastructure requirement. Founders building horizontal platforms should model sales cycles and resource requirements as enterprise from the start, regardless of deal size. Your ICP is cognitively overloaded—they won't understand your category innovation: Doctors are "under so much pressure that they just don't have any cognitive capacity left" to philosophically evaluate why AI might be difficult to implement or how infrastructure differs from technology. They need problems solved within their existing mental models. Skip the category education. Frame everything as workflow enhancement, not innovation. Let sophistication emerge through implementation, not pitch decks. Revenue doesn't equal repeatability—know when you're still in discovery mode: Despite having paying customers, Karsten explicitly states "we're not at product-market fit yet" because they're "discovering and learning things with every new laboratory hospital" around data privacy, integration, and AI deployment. The PMF signal isn't customer count or revenue—it's when the process becomes predictable, customers refer others, and you stop discovering new requirements. Hiring sales before this point scales complexity, not revenue. Regulatory friction determines market sequencing, not just market size: US governance complexity turns every deal into heavy enterprise sales with "many stakeholders," while South America proved "much more willing to move with fewer processes," making them "just much faster to adopt innovative technology." This wasn't strategy—Karsten's CTO speaks Spanish through a personal connection. But the lesson transfers: for infrastructure plays in regulated markets, test adoption velocity in lower-governance environments first to build proof points, even if TAM looks smaller on paper. In healthcare, marketing is clinical evidence—customer success creates your GTM flywheel: Karsten spends minimal time on marketing because beyond the first 5-10 users, doctors "want to see clinical evidence, they want to see papers, they want to see maybe that a friend of theirs is using it." Marketing in healthcare isn't content or demand gen—it's peer validation and published proof. Founders should structure early customer engagements to generate this evidence, not just revenue. The "marketing sales flywheel really does kick in much more once you have product market fit" because PMF enables the evidence generation required for credibility. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Didi spent five years building a product that no one really wanted. He raised $10 million, tried endless pivots, and was known as the "black sheep" of his investors' portfolio. Then, with his back against the wall, he made one final bet on a boring, unsexy market: FP&A for Excel users.In this episode, Didi breaks down how that final pivot turned into a rocket ship. He reveals why he sold cheap monthly contracts to prove demand, how he used his kids to automate LinkedIn outreach, and why targeting the market everyone else ignores (Excel lovers) was the key to unlocking massive growth.Why You Should ListenHow to survive 5 years of wandering before finding PMF.Why he sold $790/month contracts to validate a pivot.How to scale from $0 to $20M ARR with 100% outbound sales.Keywordsstartup podcast, startup podcast for founders, product market fit, finding pmf, pivot, B2B sales, outbound sales strategy, FP&A software, excel automation, Didi Gurfinkel00:00:00 Intro00:02:42 The First 5 Years of Wandering00:11:39 Being the "Black Sheep" of the Portfolio00:14:12 Identifying the FP&A Opportunity00:20:55 The Pivot: Selling $790/Month Contracts00:30:30 Scaling from $1M to $20M with Outbound00:33:18 Why the Mid-Market is Wide Open00:34:22 The Moment of True Product Market FitSend me a message to let me know what you think!

Empathy is pioneering bereavement care as an enterprise benefit, transforming how employers and financial institutions support employees during life's most challenging transitions. Working with 9 of the top 10 life insurance carriers in the US and Canada—covering over 40 million people—Empathy created a new category by combining grief support with practical logistics like probate navigation, account deactivation, and estate settlement. In a recent episode of BUILDERS, we sat down with Ron Gura, Co-Founder & CEO of Empathy, to learn how the company went from testing five verticals simultaneously to dominating life insurance, then leveraged the group life/employer overlap to expand into employee benefits. Topics Discussed: Testing five enterprise verticals simultaneously to find product-market fit Landing New York Life through their venture arm and innovation team Why life insurance carriers need to be risk-averse (and how to work with that reality) The strategic overlap between group life insurance and employee benefits Investing in brand at seed stage when your barrier to entry is psychological aversion Navigating dual audiences: decision-makers in their workday versus end users in crisis Expanding from loss to adjacent life transitions like disability leave and estate planning GTM Lessons For B2B Founders: Run parallel vertical tests with focus constraints, not sequential exploration: Ron identified 10+ potential verticals but intentionally tested exactly five simultaneously—hospices, funeral homes, employers, and two others before life insurance emerged as the winner at position five. This parallel testing with artificial constraints forces prioritization while dramatically compressing time-to-insight. Sequential testing would have meant potentially cycling through five failed pilots before discovering their strongest market. B2B founders with horizontal platforms should pick their top 3-5 verticals and run focused pilots in parallel, accepting that this burns more resources upfront but eliminates the risk of quitting before finding your wedge. Map the ecosystem overlap between buyer personas before choosing your wedge: Empathy's expansion from life insurance to employers wasn't growth strategy—it was recognizing an architectural reality. Half their carriers sell group life, meaning MetLife doesn't sell to consumers at metlife.com but exclusively to employer groups. When Amanda at Paramount loses her sister (not covered by insurance), she calls Paramount HR. When her husband dies (covered by MetLife group policy), the beneficiary calls MetLife. Same end user, two different enterprise entry points into the same moment. B2B founders should map these triangular relationships before choosing their wedge vertical. The question isn't just "who has budget?" but "who else touches this user in adjacent contexts?" Brand investment at seed stage is product strategy when fighting cognitive aversion: Ron's insight: "The barrier to entry isn't regulatory and isn't technology. It's us humans trying really hard not to think about our own mortality." This isn't a marketing problem—it's a fundamental go-to-market blocker. The company made what most would consider Series A investments (premium domain, design system, tone/voice framework) at seed stage specifically because brand reduces psychological friction to adoption. Contrast this with Monday.com starting as "daPulse" and rebranding years into success. B2B founders addressing taboo topics (death, mental health, financial distress, relationship issues) should model brand as a core distribution lever, not post-PMF polish. In deeply human categories, buyer's lived experience is your demo: Enterprise buyers at Citibank, MetLife, or Google aren't experiencing crisis during the sales cycle—they're evaluating ROI in their normal workday. But as Ron noted, "Everyone we're talking to...they're humans. They have parents, they had loss, they went through probate." The most common response after seeing the product: "Damn, I wish you called me a few months ago. I needed this a year ago with my mom." This turns product demo into personal recognition. B2B founders in universal human experience categories (caregiving, bereavement, parental leave, financial stress) should structure discovery and demo to activate buyer's memory of their own experience, not just their budget authority. Category creation is a resource-attraction strategy that trades speed for competitive exposure: Ron explicitly acknowledged: "There's pros and cons to defining a category. It's helpful when you attract resources, talent, capital. It also creates very fertile ground for a number two sympathy.com to come along and learn from this podcast...what to go after." Category leadership accelerates recruiting and fundraising by providing narrative clarity, but it simultaneously publishes your playbook. Every hiring blog post, podcast appearance, and positioning document teaches future competitors which verticals to target and which to avoid. B2B founders should treat category creation as a conscious bet: trade competitive opacity for talent/capital velocity. If you're not ready to defend your position, stay in stealth longer. Bridge new categories to existing budget lines through analogous benefits: When entering new verticals beyond life insurance, Ron doesn't educate from zero. With employers, he positions bereavement care alongside caregiving solutions, fertility programs, and parental leave: "This is a life transition happening in my own intimate house. Just like a new baby. I have new duties now." This isn't metaphor—it's budget mapping. Bereavement care gets evaluated against existing family benefits spending, not created from scratch. B2B founders in new categories should identify which existing line item their solution logically extends, then structure ROI narratives around reallocation, not net-new budget creation. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

2月にシリーズAラウンドでGCPからリード出資させていただいた化学産業の情報基盤をつくるSotas株式会社の代表取締役社長・吉元裕樹さんをゲストにお迎えしました。化学メーカー、自動車メーカー、そしてITスタートアップと多彩なキャリアを歩んできた吉元さんが、なぜ日本の基幹産業であり、巨大かつ伝統的な「化学業界」のDXに挑むことになったのか。「Sotas化学調査」や「Sotasデータベース」を展開し、市場の強い要望に引っ張られる形で圧倒的なPMFを達成。売上成長率2800%という急成長を遂げ、さらにはサプライチェーンを統合する国家プロジェクト（CMP）にスタートアップとして唯一選出されるに至った背景には、業界への深いリスペクトと、「日本の製造業の競争力を強化したい」という強い熱意がありました。シリーズAでの10億円の資金調達を経て、SaaSの枠を超えた「コングロマリット化」とデータ基盤の社会実装へと突き進むSotasの現在地と、これからの挑戦について深掘りしています！■概要Sotas事業概要吉元さん自己紹介創業の経緯ー「化学業界への恩返し」から始まった、未開拓の巨大産業DXへの挑戦Sotasが挑む化学業界の課題と、VCから見たテーマの魅力市場の要望に引っ張られてたどり着いた「圧倒的PMF」と、国家プロジェクト選出の舞台裏SaaS事業から顧客の収益ドライバー創出へ。Sotasの求める人物像■参考プレスリリース：化学産業の情報基盤をつくるSotas、シリーズAラウンド1stクローズで 10億円の資金調達を実施https://prtimes.jp/main/html/rd/p/000000028.000107222.html■プロフィールSotas株式会社 代表取締役 吉元 裕樹DIC、日産自動車、ITスタートアップを経て、2022年にSotas株式会社を創業。化学とITの知見を生かし、化学産業の各課題を解決するSaaS事業を展開。現在「Sotas化学調査」「Sotasデータベース」の2サービスの開発・提供を行う。また、サービス間のデータ連携による提供価値を複層化、化学産業の全体最適を目指している。「ウラノス・エコシステムの実現のためのデータ連携システム構築・実証事業」の大型実証にも採択。GCP パートナー エムレ 湯浅 秀和GCP Value-up Professional 水野 由貴

Miranda Nover is the Co-founder and CEO of Fort Health. Fort builds wearables that automatically track strength training for people who care about longevity.This is a new format I'm experimenting with. It's the first time I've had a Banana portfolio company founder on the show while they're still at the pre-seed stage. When I surveyed my subscribers a few weeks ago, you were most interested in more early stage VC-backed founders, and I'd love your feedback on what you think of this.Miranda is still very much working through the idea maze and iterating on the Fort product. We talk about the megatrends driving consumer health, why she's building a company that helps people get stronger, and everything she's learned getting a hardware company off the ground.She's also in the middle of the current YC batch, and gives an inside look at what it's been like and if she'd recommend it to other founders.Thank you to Numeral and Flex for supporting this episode.Try Numeral, the end-to-end platform for sales tax and compliance: https://www.numeral.comSign-up for Flex Elite with code TURNER, get $1,000: https://form.typeform.com/to/Rx9rTjFzTimestamps:(3:37) Importance of strength training(6:34) Benefits of being strong(10:37) Evolution of Fort's hardware(15:58) Automating workout tracking(19:29) Two types of strength trainers(25:30) Building the strength company(27:26) How healthcare is consumerizing(40:43) Lessons building batteries at Tesla(44:56) Hardest parts about building a hardware startup(51:01) Adventures in vibe coding(57:54) How to use Twitter as a founder(1:02:09) The launch video industrial complex(1:08:03) What it's like doing YC(1:10:19) Selling crayons in 3rd grade, Lemonade stands(1:14:41) Miranda's best vintage finds(1:16:44) How Turner evolved as a VC(1:22:22) Turner's early social media PMF(1:28:53) Inventing shitpostingReferencedTry Fort: https://www.fort.cx/Follow MirandaTwitter: https://x.com/mirandanoverLinkedIn: https://www.linkedin.com/in/mirandanoverFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Jay was running a respectable AI startup with $3M ARR. But he knew it wasn't a venture-scale rocket ship. So, he decided to fire all his customers, pivot the entire company, and bet everything on a new vertical: legal AI for plaintiff attorneys.Eve went from zero to unicorn status in under two years, raising $100M at a $1B valuation. In this episode, Jay breaks down the brutal reality of pivoting a revenue-generating company, how to achieve "demo shock" in an antiquated industry, and why 4-hour user sessions were the first sign that he had struck gold.Why You Should ListenHow threatening to shut down your product can reveal PMF.Why firing all your existing customers might be the only way to scale.How to achieve a 40% conversion rate from cold outreach to demo.Why you should target mid market instead of enterprise if you want to deploy AI fast.Keywordsstartup podcast, startup podcast for founders, product market fit, finding pmf, pivot, legal tech, AI startup, B2B sales, unicorn startup, Jay Madheswaran, Eve00:00:00 Intro00:02:27 From VC to Founder00:08:42 The First Idea: RPA for NLP00:16:52 The Hard Decision to Pivot at 3M ARR00:24:26 Product Discovery While Still Supporting Old Customers00:33:56 40 Percent Conversion from Cold Outreach00:39:56 Firing Customers to Find True PMF00:41:06 The 4-Hour User Session Signal00:46:05 From 1M to 10M ARR in One Year00:49:11 The Moment of True Product Market FitSend me a message to let me know what you think!

David Choi and Conor Moore are CoFounders of Permian Labs, the builders behind USDai.AI infrastructure is projecting trillions of dollars in CapEx spend, but there's a problem: traditional finance can't keep up. Banks move too slow. Private credit funds can't scale. The most important commodity in the world has no liquid debt market.USDai is filling this gap by financing AI infrastructure with GPU-backed loans, offering stablecoin depositors 10-15% APR. David and Conor break down how they're using DeFi rails and tokenization to create liquid debt markets for GPUs, enabling institutional borrowers to access capital and retail users to earn yield on productive AI infrastructure.In this episode, we cover:+ Why trillions in AI CapEx can't get traditional financing+ How USDAI structures loans against GPUs, not businesses+ Why this could become "the interest rate of artificial intelligence"+ Their two-token model: USDai vs. sUSDai------

This episode breaks down an article by Jason Cohen, founder of WP Engine and SmartBear, outlining his step-by-step roadmap from idea to product-market fit (PMF) for startups, especially DevTools. His 8 step roadmap provides insights on personal fit, market validation, customer interviews, building an SLC (simple, lovable, complete) MVP, sales focus, retention, prioritization, and founder psychology, drawing from Cohen's unicorn success and pitfalls to avoid.Links:   • Jason Cohen    •  WP Engine   •  Smart Bear    •  Jason Cohen's articleThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. 

Louis Jonckheere is een serieel tech-ondernemer: mede-oprichter van In The Pocket en Showpad, voorzitter van Vlerick Business School en CEO van technologiehub Wintercircus. We zoomen ook in op zijn overstap naar Aikido Security, dat onlangs een unicorn werd. Verwacht pure ondernemersverhalen, no-nonsense adviezen en een masterclass in ondernemerschap.In dit gesprek:- Wat is de echte unfair advantage voor start-ups?- Hoe moet je pitch eruit zien om investeringen op te halen?- Wat is het verhaal achter het Wintercircus en wat is de ambitie?- Wat zijn de fundamenten om je bedrijf te schalen voorbij €100M?- Hoe ging Aikido Security op 3 jaar naar een waardering van 1 miljard?En nog zoveel meer.----------- Partners:

This Week In Startups is made possible by:Quadratic - http://quadratic.ai/twistToday's show: Don't get distracted! Here are the MOST CRUCIAL aspects of running a startup, where founders need to keep their full and uninterrupted focus.- Make sure you're saving up your cash- Why you need to just get started and build SOMETHING- Trust and reliability is EVERYTHING for new products- Why distribution should be your top priorityDownload all this practical and tactical startup advice from seasoned veterans Jason Calacanis, Amanda Bradford, and William P. Barnes in this Tokyo edition of TWiST.Timestamps:(00:00) Amanda and Will's big takeaways from Founder U in Saudi Arabia and now Tokyo(3:19) Why cash flow management is so important before you find PMF(5:57) Why first-time founders get the order of operations wrong(6:45) Just build SOMETHING, even if it's taped together(11:19) “Focus is everything in the early stages”(14:17) From a wedge to a bridge(15:50) Quadratic - Bringing the productivity boost of AI into your spreadsheets. Visit http://quadratic.ai/twist to sign up and use the code TWIST to get one free month of their pro tier subscription.(18:45) What to put on your “Not Right Now” list(19:11) Embracing simplicity(22:00) The importance of trust and reliability (especially for Uber!)(28:12) Why innovation needs a constraining variable(33:40) To really drive word of mouth, you have to overdeliver(36:32) Distribution is the primary job of a founder/CEO(38:19) Some of the panel's favorite distribution hacks(45:54) Why Jason respects Japan's commitment to excellence and competency(49:38) Looking for “high slope” in early employees(53:11) Transitioning your team from early-stage startup to growth(57:45) Whoever writes it down gets credit for the idea(58:04) Q: Has the meaning of money changed for the panel now that they've had successful exits?*Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com/Check out the TWIST500: https://twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcp*Follow Lon:X: https://x.com/lons*Follow Alex:X: https://x.com/alexLinkedIn: https://www.linkedin.com/in/alexwilhelm/*Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis/*Thank you to our partners:(15:50) Quadratic - Bringing the productivity boost of AI into your spreadsheets. Visit http://quadratic.ai/twist to sign up and use the code TWIST to get one free month of their pro tier subscription.Check out all our partner offers: https://partners.launch.co/

This episode breaks down Marc Andreessen's 2007 article on why market matters most in startups, plus some great wisdom from Michael Seibel on spotting real PMF through explosive growth and customer pull.Links:   •  Marc Andreessen's article   •  Michael Seibel's post   •  Product Market Fit collapseThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.

This is a special episode from the Neon Fund.In 2025, the US saw $1.8 trillion worth of M&A deals, around 25× more than India. But India's startup ecosystem is much younger, which makes every acquisition a playbook for founders on process, pricing leverage, and stakeholder management.Neon backed Zenduty in 2020, when the founders had been bootstrapping profitably for two years and were already growing at a pace many VC-backed startups aspire to.Today, founders Ankur Rawal and Vishwa Krishnakumar join Siddhartha, Partner at Neon, to discuss one of the most untalked acquisitions of 2025.Over a 10-year journey, Zenduty pivoted to SRE in 2020. Vishwa and Ankur also share insights on the future of the DevTools space, which they believe will always be a strong choice to build great products, because engineers are among the hardest end users to please.This episode is a founders' view on how acquisitions work in Indian SaaS.00:00 – Trailer01:00 – Initial years of a decade-long journey07:12 – How Zenduty chose its investors11:04 – How much should founders dilute?12:24 – Building with profitability before & after fundraise14:45 – Six years of survival before the pivot17:01 – Why the pivot to the SRE space?18:39 – How Zenduty differentiated from PagerDuty19:12 – End users are the toughest to please in engineering20:39 – Is market attractive if biggest player is valued only $1.5B?25:22 – Why acquisition and not a Series A?27:18 – The process before acquisition29:23 – How pricing negotiations work31:51 – Should devtool companies build from India or US?34:58 – Three types of connects at physical events37:06 – What physical presence at events signals39:06 – Founders' feedback on Neon Fund41:41 – “Don't build in silence”43:50 – How to build a core AI-native company today47:54 – Do first-time founders have an edge in the AI era?52:08 – Cost to PMF has drastically gone down54:48 – What hard problems are startups solving today?55:37 – Why are acquisitions rare in India?1:00:20 – How US investors are facilitating M&As1:01:14 – How to make your brand visible to potential acquirers-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Think recovery tools only work if you use everything at once? Jake Ross breaks down how combining PEMF, far infrared, red light, & natural gemstones creates consistent gains in energy, nervous system balance, and performance, without complicated routines. Meet our guest Jake Ross is part of the leadership and growth marketing team at HealthyLine, where he helps translate advanced recovery technologies into real-world routines for athletes and health-focused individuals. He focuses on product education, customer experience, and how PEMF, infrared, red light, and gemstone therapies support recovery and longevity. Thank you to our partners Outliyr Biohacker's Peak Performance Shop: get exclusive discounts on cutting-edge health, wellness, & performance gear Ultimate Health Optimization Deals: a database of of all the current best biohacking deals on technology, supplements, systems and more Latest Summits, Conferences, Masterclasses, and Health Optimization Events: join me at the top events around the world FREE Outliyr Nootropics Mini-Course: gain mental clarity, energy, motivation, and focus Key takeaways NFL athletes like Christian Jones use jet mats to support daily recovery, showing recovery now ranks as high as training Pro athletes extend peak performance years by prioritizing recovery over more volume or intensity Consistent recovery routines drive measurable sleep score gains, rising from the low 70s to 86+ PMF devices emit controlled low-intensity waves tuned to biological function, unlike uncontrolled environmental EMFs PMF frequencies range from 0–15 Hz, with Earth's Schumann resonance at 7.83 Hz as a biological reference point Users tune PMF protocols for relaxation, grounding, productivity, or physical recovery based on nervous system needs HealthyLine Jet Series mats stack PMF, far infrared, red light, negative ions, & gemstone heat in one session Far infrared heat penetrates deeply to increase blood flow & support joints, tissues, & recovery Heated gemstones like amethyst, jade, & tourmaline release negative ions that improve air quality & relaxation Consistent stacked recovery beats one-off sessions, reinforcing the lesson that recovery works best as a habit, not a hack Episode highlights 00:00 EMFs as an invisible performance stressor in modern environments 04:28 PEMF explained and how controlled frequencies support recovery 10:58 Why recovery habits drive longevity and performance 22:17 Building sustainable recovery routines that actually stick 38:50 Stacking PEMF, infrared, red light, and natural elements 52:42 Long-term recovery mindset and avoiding quick-fix thinking   Links Watch it on YouTube: https://youtu.be/ruOssswPbo0  Full episode show notes: outliyr.com/246 Connect with Nick on social media Instagram Twitter (X) YouTube LinkedIn Easy ways to support Subscribe Leave an Apple Podcast review Suggest a guest Do you have questions, thoughts, or feedback for us? Let me know in the show notes above and one of us will get back to you! Be an Outliyr, Nick

Harman went from cold-calling hotels 100 times a day to building the category-defining guest management platform for the hospitality industry. Canary built a $600M company by first solving one tiny, annoying problem: paper credit card authorization forms.In this episode, Harman breaks down how a simple digital form became the wedge into thousands of hotels. He reveals why they stuck with outbound sales long after hitting millions in revenue, the terror of collecting physical checks during the first week of COVID, and the exact moment he knew they had hit product-market fit.Why You Should ListenThe "Activated Hair on Fire" framework: How to turn a latent problem into a must-have purchase.Why outbound sales (and cold calling) is often your top early growth channel.How to use a simple, "unscalable" wedge to unlock a massive market.Why you should celebrate the lows: A counterintuitive take on managing founder psychology.The story of signing 200+ customers in a single day (and finding true PMF).Keywordsstartup podcast, startup podcast for founders, product market fit, finding pmf, vertical saas, outbound sales, cold calling strategies, early stage growth, b2b sales, hospitality tech00:00:00 Intro00:02:13 From Management Consulting to Hotel Tech00:11:32 The Paper Form that Launched a Company00:17:35 The Activated Hair on Fire Framework00:24:26 Landing the First Customer via Cold Call00:28:21 Applying to YC 00:32:35 Making 100 Cold Calls a Day00:43:42 The COVID Cash Flow Panic00:48:27 Signing 200 Customers in One DaySend me a message to let me know what you think!

Jason Cohen is a four-time founder (including two unicorns, one being WP Engine) and an investor in over 60 startups, and has been sharing his lessons on company building at A Smart Bear for nearly 20 years. In this episode, Jason shares his methodical five-step framework for diagnosing stalled growth—a problem that faces almost every team.We discuss:1. Jason's five-step framework: logo retention, pricing, NRR, marketing channels, target market2. A small tweak that'll double response rates on your cancellation surveys3. Why “it's too expensive” is almost never the real reason customers cancel4. The “elephant curve” of growth5. How repositioning the same product can increase revenue 8x6. When to reconsider if growth is even the right goal for your business—Brought to you by:10Web—Vibe coding platform as an APIStrella—The AI-powered customer research platformBrex—The banking solution for startups—Episode transcript: https://www.lennysnewsletter.com/p/why-your-product-stopped-growing—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Jason Cohen:• Preorder Jason's book: https://preorder.hiddenmultipliers.com/• X: https://x.com/asmartbear• LinkedIn: https://www.linkedin.com/in/jasoncohen• Blog: https://longform.asmartbear.com• Website: https://wpengine.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jason Cohen(05:19) Jason's writing journey(08:25) Questions to ask when your product stops growing(18:17) Getting real customer feedback(20:27) Analyzing cancellation reasons(26:54) Onboarding and activation(29:35) Quick summary(35:46) Revisiting pricing strategies(41:46) Positioning strategies(47:52) Why pricing is inseparable from your strategy(52:06) The importance of net revenue retention (NRR)(01:00:25) Asking whether or not this is good for the customer(01:04:34) Leveraging existing customers(01:06:42) Are your acquisition channels saturated? The “elephant curve”(1:09:41) Why all marketing channels eventually decline(01:12:04) Direct vs. indirect marketing channels(1:13:36) Getting creative with new channels(01:19:04) Do you actually need to grow?(01:25:57) Deciding when to quit(01:29:27) Book announcement(01:33:21) AI corner(01:34:35) Contrarian corner(01:37:43) Lightning round and final thoughts—Referenced:• Tyler Cowen's website: https://tylercowen.com• How to Perform a Customer Churn Analysis (and Why You Should): https://www.groovehq.com/blog/learn-from-customer-churn• Linear: https://linear.app• Jira: https://www.atlassian.com/software/jira• Patrick Campbell's post on X about pricing: https://x.com/Patticus/status/1702313260547006942• The art and science of pricing | Madhavan Ramanujam (Monetizing Innovation, Simon-Kucher): https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan• Pricing your AI product: Lessons from 400+ companies and 50 unicorns | Madhavan Ramanujam: https://www.lennysnewsletter.com/p/pricing-and-scaling-your-ai-product-madhavan-ramanujam• Pricing your SaaS product: https://www.lennysnewsletter.com/p/saas-pricing-strategy• M&A, competition, pricing, and investing | Julia Schottenstein (dbt Labs): https://www.lennysnewsletter.com/p/m-and-a-competition-pricing-and-investing• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr• Buffer: https://buffer.com• AG1: https://drinkag1.com• How to find hidden growth opportunities in your product | Albert Cheng (Duolingo, Grammarly, Chess.com): https://www.lennysnewsletter.com/p/how-to-find-hidden-growth-opportunities-albert-cheng• How Duolingo reignited user growth: https://www.lennysnewsletter.com/p/how-duolingo-reignited-user-growth• The Elephant in the room: The myth of exponential hypergrowth: https://longform.asmartbear.com/exponential-growth• HubSpot: https://www.hubspot.com• Zigging vs. zagging: How HubSpot built a $30B company | Dharmesh Shah (co-founder/CTO): https://www.lennysnewsletter.com/p/lessons-from-30-years-of-building• Adjacency Matrix: How to expand after PMF: https://longform.asmartbear.com/adjacency/• Ecosystem is the next big growth channel: https://www.lennysnewsletter.com/p/ecosystem-is-the-next-big-growth• ChatGPT apps are about to be the next big distribution channel: Here's how to build one: https://www.lennysnewsletter.com/p/chatgpt-apps-are-about-to-be-the• 10 contrarian leadership truths every leader needs to hear | Matt MacInnis (Rippling): https://www.lennysnewsletter.com/p/10-contrarian-leadership-truths• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• Geoffrey Moore on finding your beachhead, crossing the chasm, and dominating a market: https://www.lennysnewsletter.com/p/geoffrey-moore-on-finding-your-beachhead• ER on Prime Video: https://www.amazon.com/ER-Season-1/dp/B0FWK5WJQ4• The Pitt on Prime Video: https://www.amazon.com/The-Pitt-Season-1/dp/B0DNRR8QWD• Wispr Flow: https://wisprflow.ai• Anker: https://www.anker.com—Recommended books:• Will: https://www.amazon.com/Will-Smith/dp/1984877925• Monetizing Innovation: How Smart Companies Design the Product Around the Price: https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867• Hidden Multipliers: Small Things That Accelerate Growth: https://preorder.hiddenmultipliers.com• On Writing Well: The Essential Guide to Mastering Nonfiction Writing and Effective Communication: https://www.amazon.com/Writing-Well-Classic-Guide-Nonfiction/dp/0060891548• Crossing the Chasm, 3rd Edition: The Updated Version of the Insightful Guide on Bringing Cutting-Edge Products to the Mainstream: https://www.amazon.com/Crossing-Chasm-3rd-Disruptive-Mainstream/dp/0062292986—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Amplio operates a two-sided marketplace that helps manufacturers monetize surplus inventory and decommissioned industrial equipment rather than writing off assets or paying for disposal. The company has won contracts with GM and SpaceX despite competing against liquidators with 30-year local relationships. In a recent episode of BUILDERS, we sat down with Trey Closson, Co-Founder and CEO of Amplio, to unpack how the company executed a complete business model pivot from supply chain risk software to marketplace, discovered that enterprise deals close faster than SMB despite conventional wisdom, and built repeatable GTM motions in a fragmented $100B+ market previously dominated by local operators. Topics Discussed: Executing Amplio's pivot from supply chain risk software to surplus inventory marketplace Moving four truckloads of inventory through a WeWork to prove the business model Closing GM and SpaceX inbound from Google Ads as the PMF validation signal Displacing 30-year incumbent relationships through corporate + local dual threading Why enterprise contracts closed faster than SMB deals in Amplio's specific context Scaling beyond founder-led sales to repeatable AE motions Operating a two-sided marketplace: supply acquisition strategy vs. demand conversion GTM Lessons For B2B Founders: Manual heroics prove economics before automation: When a customer offered Amplio $25 million in surplus inventory, Trey had no warehouse, no logistics infrastructure, and no playbook. What was supposed to be four pallets became four full truckloads delivered to their WeWork. Trey and one employee physically moved inventory boxes off pallets into their office space, then figured out how to sell it while the WeWork management threatened eviction. The core insight: "the first time solving a problem, it doesn't need to be an automated, efficient process, it just needs to be okay. A customer has a problem, we need to figure out a way to solve that problem." Only after proving they could profitably solve the problem multiple times did they invest in automation and efficiency. For founders, the implication is clear—delay infrastructure investment until you've manually proven unit economics and repeatability, even if execution requires unsustainable effort. True PMF signals come from zero-relationship wins: Trey leveraged 15 years of supply chain relationships to secure initial customers and build product infrastructure. But he identifies the precise PMF inflection point: "middle of last year, we had both GM and SpaceX respond to a Google Ad." These companies had zero connection to Trey or his co-founder, found Amplio through SEM, and chose them over traditional liquidators they'd worked with for years. This is the distinction between "my network will buy from me" and "the market will buy from us." Founders should use their Rolodex to achieve velocity and prove the concept, but recognize that true product-market fit only exists when customers with no founder relationship choose your solution over established alternatives. Enterprise velocity depends on payment direction and urgency profile: Amplio deliberately focused on enterprise after being told by multiple founders to avoid "hunting whales." They discovered enterprise closed faster than SMB for three structural reasons. First, SMBs had unrealistic recovery expectations—wanting $900K back on $1M inventory when market reality is cents on the dollar, creating unresolvable expectation gaps. Second, enterprises had the problem across 100+ facilities with no dedicated owner and urgent mandates from finance or supply chain leadership. Third, because Amplio pays customers rather than charging them, legal review velocity increased dramatically. As Trey explains: "the lawyers thankfully determine, because we're not getting paid by them, that there's low risk for them in terms of signing a contract with us." Founders should map their specific deal structure and customer urgency profile rather than defaulting to SMB-first based on generic advice. Displace entrenched relationships through dual-threading: The surplus liquidation market is hyper-fragmented with hundreds of thousands of local liquidators, many holding 30-year plant-level relationships. Amplio's breakthrough: "partnering together with that person at the corporate level we can indicate not only can we solve the problem locally, but we can also do it across the entire enterprise." They pair the local plant manager with corporate procurement or finance leadership, demonstrating local problem-solving plus enterprise-wide scalability that local liquidators cannot match. This dual-threading strategy neutralizes the incumbent's relationship advantage while showcasing the efficiency and consistency that corporate leadership values. For founders entering relationship-driven markets, identify the corporate stakeholder whose enterprise-wide objectives trump individual facility loyalty. Accelerate trust through predictable execution in low-NPS markets: Industrial liquidation is a "really low NPS industry—nobody loves working with their liquidator." In markets with poor customer satisfaction and commoditized offerings, trust accelerates when you focus on "say-do ratio"—if you commit to something, execute it. Amplio often solves adjacent problems outside their core offering and frequently removes inventory from warehouses faster than economically optimal to make customers "look like an absolute hero." This over-delivery in low-satisfaction markets creates disproportionate differentiation. The tactical implementation: understand what problems the organization is trying to solve beyond your core product, find ways to solve those problems even if not monetizable, and prioritize making your champion successful over optimizing every transaction. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Send us a textGuest: Kevin Jacobson, CEO at Foxen -- Most SaaS companies try to scale by adding headcount and channels. Foxen scaled by tightening fundamentals.In this episode, Kevin Jacobson, CEO of Foxen, joins host Ken Lempit to explain how an overlooked market — multifamily housing — became a durable SaaS growth opportunity through operational discipline and relationship-driven GTM.Kevin breaks down why traditional industries lag in SaaS adoption, why consistency matters more than speed, and how Foxen scaled through direct sales, referrals, and systems built to support growth. He also shares lessons from raising growth equity and why systems, not people, ultimately unlock scale.Key takeaways:Underserved markets reward execution over hypeConsistency precedes scalable SaaS growthDirect sales still win in relationship-driven marketsSystems, not headcount, enable scaleIf you're a SaaS leader selling into traditional industries or rethinking how growth really happens after PMF, this episode delivers a grounded, operator-first perspective.---Not Getting Enough Demos? Your messaging could be turning buyers away before you even get a chance to pitch.

My guests today are Tom Digan and Greg Stewart. Tom is the co-founder of Ladder, and Greg is its CEO. Ladder was my first angel investment. What followed over the next seven years is one of the most unlikely and dramatic business stories I've been a part of. Today, Ladder is the number one grossing fitness app in the App Store, approaching $100M in ARR with more than 300,000 paying members. But the path from near death to dominance involved debt collectors, leadership changes, and a full reset during the pandemic. Tom and Greg built Ladder by being relentlessly empirical about their customers, ruthless about prioritization, raising money wherever they could, and doing whatever it took when most founders would have quit. We cover the messy early years when survival meant negotiating creditors, how they found PMF by reading thousands of app store reviews, and how they built a TikTok growth engine with no performance marketing experience. They share their long-term vision for becoming the category winner for health and fitness and the impact of AI and GLP-1s on their business. This is a conversation about how hard it really is to build something valuable, told by two people who lived through all of it. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠ to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vanta. Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit vanta.com/invest.  ----- This episode is brought to you by Rogo. Rogo is an AI-powered platform that automates accounts payable workflows, enabling finance teams to process invoices faster and with greater accuracy. Learn more at Rogo.ai/invest. ----- This episode is brought to you by ⁠WorkOS⁠. WorkOS is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit ⁠WorkOS.com⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps (00:00:00) Welcome to Invest Like The Best  (00:04:26) Episode Intro (00:05:45) Ladder: The #1 Fitness App (00:09:28) The Messy, Early Years (00:14:47) Sponsors (00:16:20) The Darkest Point (00:18:17) Why Greg Joined Ladder (00:19:45) The Turning Point: Ladder 2.0 (00:21:57) The Key to Negotiating with Creditors (00:23:16) Fundraising Challenges and Strategies (00:25:50) Developing Ladder Teams (00:31:31) Listen to Your Customers (00:32:57) Launching Nutrition (00:38:53) Sponsors (00:39:31) Don't Listen to Investors on Product Feedback (00:40:18) The Cave Process (00:43:13) Crossing the Chasm (00:43:53) How to Crack TikTok (00:51:10) The Content Frontier (00:52:07) Controlled Bets at Scale (00:54:19) Why you should Build a B2C Company (00:57:37) The Impact of AI and GLP-1s (01:02:32) Sponsors (01:02:53) Staying Focused on the Core Product (01:05:00) Building the System of Record for Health and Fitness (01:09:45) What It's Like Talking to Investors Now (01:12:32) The Kindest Thing

Jake founded Serval in April 2024— by Dec 2025 he'd raised a $75M Series B from Sequoia at a $1B valuation.He didn't look for a "wedge" or a "niche." He looked at ServiceNow—a $160B, 20+ year-old incumbent that everyone IT team relies on—and rebuilt it from the ground up in a YEAR. In this episode, Jake reveals the audacity behind building a full-platform replacement from Day 1, why he spent months building in the dark with zero revenue, and how he achieved a 50% demo-to-close rate on six-figure enterprise deals.Why You Should ListenHow to go from incorporation to a $1B valuation in just 18 months.The psychological shift in sales calls that proves PMF.How to build a demo so compelling that 50% buy on the spot.Why you no longer need to find a small wedge to win post Gen AI.The specific question that stops customers from giving you generic feedback.Keywordsstartup podcast, startup podcast for founders, hypergrowth, zero to one, unicorn startup, Sequoia Capital, replacing legacy software, enterprise sales strategy, ServiceNow competitor, Jake Stauch00:00:00 Intro00:03:25 Why "Hair on Fire" Problems Matter00:06:58 Learning What Winning Feels Like at Verkada00:14:05 100+ Customer Discovery Calls00:18:12 The One Question That Unlocks Real Pain00:23:48 Why No-Code Workflows Fail00:28:45 Taking Risks on AI Model Improvements00:35:49 From $0 to Six-Figure ACVs in 6 Months00:39:00 The Strategy to Rip and Replace ServiceNow00:47:30 The "Rounding Up" Signal of PMFSend me a message to let me know what you think!

This week, Dougie DeLuca joins the Hivemind team to discuss his recent piece "Crypto Is Dead". We deep dive into the shift that is happening within crypto as we head into 2026, why is sentiment so bad, where to allocate in 2026, where crypto has found PMF and more. Enjoy! -- Follow Dougie: https://x.com/DougieDeLuca Follow Jose: https://x.com/ZeMariaMacedo Follow Jason: ⁠https://x.com/3xliquidated⁠ Follow Yan: https://x.com/YanLiberman Follow Empire: ⁠https://x.com/theempirepod⁠ Subscribe on YouTube: ⁠https://bit.ly/4jYEkBx⁠ Subscribe on Apple: ⁠https://bit.ly/3ECSmJ3⁠ Subscribe on Spotify: ⁠https://bit.ly/4hzy9lH⁠ -- Crypto Is Dead: https://x.com/DougieDeLuca/status/2000957512862884100 -- Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: ⁠https://blockworks.co/newsletter/⁠ -- Timestamps: (0:00) Introduction (0:56) Is Crypto Dead? (12:20) Why Is Sentiment So Bad? (28:52) Crypto's Path Forward (34:21) Where To Allocate In 2026? -- Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, the Hivemind team, and our guests may hold positions in the companies, funds, or projects discussed.

GreenLite delivers private construction plan review as an alternative to traditional city permitting processes. After spending six months testing both sides of the construction permitting transaction, the company identified owner-developers as their ICP and built a business model around Florida's privatization legislation—legislation that has now expanded to nine additional states including Texas, Tennessee, and California. In this episode of BUILDERS, we sat down with James Gallagher, CEO and Co-Founder of GreenLite, to explore how his fifth startup leveraged regulatory shifts, rejected workflow software in favor of outcomes, and scaled by targeting chief development officers at enterprise retailers struggling with permitting delays. Topics Discussed: How GreenLite discovered architects were heavy users but wrong customers due to two-part sales dynamics Why owner-developers became the ICP after six months of customer discovery across applicants and agencies The accidental discovery of private plan review through conversations with Fort Worth and Miami-Dade agencies GreenLite's platform combining regulatory permissions, licensed AEC professionals, and AI-augmented software How natural disasters and AEC talent shortages are accelerating privatization legislation nationwide Cold email strategies that converted enterprise retailers by surfacing acute pain points GTM Lessons For B2B Founders: Map two-sided markets to find where purchasing authority and pain intersect: GreenLite pitched a CTO at a major architecture firm who responded positively but said "I just need to talk to my client, my customer." This revealed architects required approval from owner-developers despite being the heaviest product users. James pivoted to owner-developers who "carry the land, carry the construction loans" and feel revenue delays most acutely. The lesson: usage intensity doesn't equal buyer authority. In complex ecosystems, systematically test which party controls budget and feels enough pain to sign contracts independently. Recognize when procurement cycles kill early-stage validation velocity: Cities explicitly told James their "crazy procurement cycles" made early partnership impractical despite genuine interest. State and local education and government sales require specialized expertise and extended timelines that prevent rapid iteration. James chose to prove the model with private sector customers first. For founders: government can be a lucrative eventual market, but unless you have sled sales expertise and 12+ month runway per deal, validate PMF elsewhere first. Capitalize on regulatory tailwinds before markets realize they exist: Only Florida permitted private plan review when GreenLite launched in July 2022. By late 2024, nine states passed enabling legislation driven by natural disaster reconstruction needs and talent shortages in city building departments. James positioned GreenLite to ride this wave rather than selling transformation to resistant agencies. Founders should monitor legislative and regulatory changes in their verticals—new compliance requirements or permissions can suddenly open massive TAMs with minimal incumbent competition. Enterprise cold email converts when you surface non-obvious acute pain: GreenLite cold emailed chief development officers at major retail chains and quick-service restaurants with "Are you missing your openings due to permitting?" The response rate validated that permitting delays—not site selection or construction costs—were a critical path blocker for store rollout velocity. James targeted CDOs rather than real estate or design teams because they own the full development timeline. For enterprise sales: identify the executive accountable for the metric your solution impacts, then lead with how you move that specific number. Validate outcome-based models before building sophisticated workflow tools: GreenLite's customers rejected "another workflow product or system of record" that required API integrations with their ERPs and construction management systems. Instead, they wanted "faster, more predictable, more transparent permits." James built a viable business delivering finished permits through licensed professionals augmented by software, with the AI sophistication coming later. The business was "super viable well before the product was" by early 2023. For founders in industries resistant to software adoption: test whether buyers want tools to operate or outcomes to purchase—outcome-based pricing can achieve PMF faster and command premium willingness-to-pay. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

遊戲規則已經改變，未來的贏家將是最快察覺並回應產品市場契合度變化的公司。The rules of the game have changed, the companies that will win in the future detect and respond to changes in PMF the fastest. -- Hosting provided by SoundOn

TLDR: It was Claude :-)When I set out to compare ChatGPT, Claude, Gemini, Grok, and ChatPRD for writing Product Requirement Documents, I figured they'd all be roughly equivalent. Maybe some subtle variations in tone or structure, but nothing earth-shattering. They're all built on similar transformer architectures, trained on massive datasets, and marketed as capable of handling complex business writing.What I discovered over 45 minutes of hands-on testing revealed not just which tools are better for PRD creation, but why they're better, and more importantly, how you should actually be using AI to accelerate your product work without sacrificing quality or strategic thinking.If you're an early or mid-career PM in Silicon Valley, this matters to you. Because here's the uncomfortable truth: your peers are already using AI to write PRDs, analyze features, and generate documentation. The question isn't whether to use these tools. The question is whether you're using the right ones most effectively.So let me walk you through exactly what I did, what I learned, and what you should do differently.The Setup: A Real-World Test CaseHere's how I structured the experiment. As I said at the beginning of my recording, “We are back in the Fireside PM podcast and I did that review of the ChatGPT browser and people seemed to like it and then I asked, uh, in a poll, I think it was a LinkedIn poll maybe, what should my next PM product review be? And, people asked for ChatPRD.”So I had my marching orders from the audience. But I wanted to make this more comprehensive than just testing ChatPRD in isolation. I opened up five tabs: ChatGPT, Claude, Gemini, Grok, and ChatPRD.For the test case, I chose something realistic and relevant: an AI-powered tutor for high school students. Think KhanAmigo or similar edtech platforms. This gave me a concrete product scenario that's complex enough to stress-test these tools but straightforward enough that I could iterate quickly.But here's the critical part that too many PMs get wrong when they start using AI for product work: I didn't just throw a single sentence at these tools and expect magic.The “Back of the Napkin” Approach: Why You Still Need to Think“I presume everybody agrees that you should have some formulated thinking before you dump it into the chatbot for your PRD,” I noted early in my experiment. “I suppose in the future maybe you could just do, like, a one-sentence prompt and come out with the perfect PRD because it would just know everything about you and your company in the context, but for now we're gonna do this more, a little old-school AI approach where we're gonna do some original human thinking.”This is crucial. I see so many PMs, especially those newer to the field, treat AI like a magic oracle. They type in “Write me a PRD for a social feature” and then wonder why the output is generic, unfocused, and useless.Your job as a PM isn't to become obsolete. It's to become more effective. And that means doing the strategic thinking work that AI cannot do for you.So I started in Google Docs with what I call a “back of the napkin” PRD structure. Here's what I included:Why: The strategic rationale. In this case: “Want to complement our existing edtech business with a personalized AI tutor, uh, want to maintain position industry, and grow through innovation. on mission for learners.”Target User: Who are we building for? “High school students interested in improving their grades and fundamentals. Fundamental knowledge topics. Specifically science and math. Students who are not in the top ten percent, nor in the bottom ten percent.”This is key—I got specific. Not just “students,” but students in the middle 80%. Not just “any subject,” but science and math. This specificity is what separates useful AI output from garbage.Problem to Solve: What's broken? “Students want better grades. Students are impatient. Students currently use AI just for finding the answers and less to, uh, understand concepts and practice using them.”Key Elements: The feature set and approach.Success Metrics: How we'd measure success.Now, was this a perfectly polished PRD outline? Hell no. As you can see from my transcript, I was literally thinking out loud, making typos, restructuring on the fly. But that's exactly the point. I put in maybe 10-15 minutes of human strategic thinking. That's all it took to create a foundation that would dramatically improve what came out of the AI tools.Round One: Generating the Full PRDWith my back-of-the-napkin outline ready, I copied it into each tool with a simple prompt asking them to expand it into a more complete PRD.ChatGPT: The Reliable GeneralistChatGPT gave me something that was... fine. Competent. Professional. But also deeply uninspiring.The document it produced checked all the boxes. It had the sections you'd expect. The writing was clear. But when I read it, I couldn't shake the feeling that I was reading something that could have been written for literally any product in any company. It felt like “an average of everything out there,” as I noted in my evaluation.Here's what ChatGPT did well: It understood the basic structure of a PRD. It generated appropriate sections. The grammar and formatting were clean. If you needed to hand something in by EOD and had literally no time for refinement, ChatGPT would save you from complete embarrassment.But here's what it lacked: Depth. Nuance. Strategic thinking that felt connected to real product decisions. When it described the target user, it used phrases that could apply to any edtech product. When it outlined success metrics, they were the obvious ones (engagement, retention, test scores) without any interesting thinking about leading indicators or proxy metrics.The problem with generic output isn't that it's wrong, it's that it's invisible. When you're trying to get buy-in from leadership or alignment from engineering, you need your PRD to feel specific, considered, and connected to your company's actual strategy. ChatGPT's output felt like it was written by someone who'd read a lot of PRDs but never actually shipped a product.One specific example: When I asked for success metrics, ChatGPT gave me “Student engagement rate, Time spent on platform, Test score improvement.” These aren't wrong, but they're lazy. They don't show any thinking about what specifically matters for an AI tutor versus any other educational product. Compare that to Claude's output, which got more specific about things like “concept mastery rate” and “question-to-understanding ratio.”Actionable Insight: Use ChatGPT when you need fast, serviceable documentation that doesn't need to be exceptional. Think: internal updates, status reports, routine communications. Don't rely on it for strategic documents where differentiation matters. If you do use ChatGPT for important documents, treat its output as a starting point that needs significant human refinement to add strategic depth and company-specific context.Gemini: Better Than ExpectedGoogle's Gemini actually impressed me more than I anticipated. The structure was solid, and it had a nice balance of detail without being overwhelming.What Gemini got right: The writing had a nice flow to it. The document felt organized and logical. It did a better job than ChatGPT at providing specific examples and thinking through edge cases. For instance, when describing the target user, it went beyond demographics to consider behavioral characteristics and motivations.Gemini also showed some interesting strategic thinking. It considered competitive positioning more thoughtfully than ChatGPT and proposed some differentiation angles that weren't in my original outline. Good AI tools should add insight, not just regurgitate your input with better formatting.But here's where it fell short: the visual elements. When I asked for mockups, Gemini produced images that looked more like stock photos than actual product designs. They weren't terrible, but they weren't compelling either. They had that AI-generated sheen that makes it obvious they came from an image model rather than a designer's brain.For a PRD that you're going to use internally with a team that already understands the context, Gemini's output would work well. The text quality is strong enough, and if you're in the Google ecosystem (Docs, Sheets, Meet, etc.), the integration is seamless. You can paste Gemini's output directly into Google Docs and continue iterating there.But if you need to create something compelling enough to win over skeptics or secure budget, Gemini falls just short. It's good, but not great. It's the solid B+ student: reliably competent but rarely exceptional.Actionable Insight: Gemini is a strong choice if you're working in the Google ecosystem and need good integration with Docs, Sheets, and other Google Workspace tools. The quality is sufficient for most internal documentation needs. It's particularly good if you're working with cross-functional partners who are already in Google Workspace. You can share and collaborate on AI-generated drafts without friction. But don't expect visual mockups that will wow anyone, and plan to add your own strategic polish for high-stakes documents.Grok: Not Ready for Prime TimeLet's just say my expectations were low, and Grok still managed to underdeliver. The PRD felt thin, generic, and lacked the depth you need for real product work.“I don't have high expectations for grok, unfortunately,” I said before testing it. Spoiler alert: my low expectations were validated.Actionable Insight: Skip Grok for product documentation work right now. Maybe it'll improve, but as of my testing, it's simply not competitive with the other options. It felt like 1-2 years behind the others.ChatPRD: The Specialized ToolNow this was interesting. ChatPRD is purpose-built for PRDs, using foundational models underneath but with specific tuning and structure for product documentation.The result? The structure was logical, the depth was appropriate, and it included elements that showed understanding of what actually matters in a PRD. As I reflected: “Cause this one feels like, A human wrote this PRD.”The interface guides you through the process more deliberately than just dumping text into a general chat interface. It asks clarifying questions. It structures the output more thoughtfully.Actionable Insight: If you're a technical lead without a dedicated PM, or you're a PM who wants a more structured approach to using AI for PRDs, ChatPRD is worth the specialized focus. It's particularly good when you need something that feels authentic enough to share with stakeholders without heavy editing.Claude: The Clear WinnerBut the standout performer, and I'm ranking these, was Claude.“I think we know that for now, I'm gonna say Claude did the best job,” I concluded after all the testing. Claude produced the most comprehensive, thoughtful, and strategically sound PRD. But what really set it apart were the concept mocks.When I asked each tool to generate visual mockups of the product, Claude produced HTML prototypes that, while not fully functional, looked genuinely compelling. They had thoughtful UI design, clear information architecture, and felt like something that could actually guide development.“They were, like, closer to, like, what a Lovable would produce or something like that,” I noted, referring to the quality of low-fidelity prototypes that good designers create.The text quality was also superior: more nuanced, better structured, and with more strategic depth. It felt like Claude understood not just what a PRD should contain, but why it should contain those elements.Actionable Insight: For any PRD that matters, meaning anything you'll share with leadership, use to get buy-in, or guide actual product development, you might as well start with Claude. The quality difference is significant enough that it's worth using Claude even if you primarily use another tool for other tasks.Final Rankings: The Definitive HierarchyAfter testing all five tools on multiple dimensions: initial PRD generation, visual mockups, and even crafting a pitch paragraph for a skeptical VP of Engineering, here's my final ranking:* Claude - Best overall quality, most compelling mockups, strongest strategic thinking* ChatPRD - Best for structured PRD creation, feels most “human”* Gemini - Solid all-around performance, good Google integration* ChatGPT - Reliable but generic, lacks differentiation* Grok - Not competitive for this use case“I'd probably say Claude, then chat PRD, then Gemini, then chat GPT, and then Grock,” I concluded.The Deeper Lesson: Garbage In, Garbage Out (Still Applies)But here's what matters more than which tool wins: the realization that hit me partway through this experiment.“I think it really does come down to, like, you know, the quality of the prompt,” I observed. “So if our prompt were a little more detailed, all that were more thought-through, then I'm sure the output would have been better. But as you can see we didn't really put in brain trust prompting here. Just a little bit of, kind of hand-wavy prompting, but a little better than just one or two sentences.”And we still got pretty good results.This is the meta-insight that should change how you approach AI tools in your product work: The quality of your input determines the quality of your output, but the baseline quality of the tool determines the ceiling of what's possible.No amount of great prompting will make Grok produce Claude-level output. But even mediocre prompting with Claude will beat great prompting with lesser tools.So the dual strategy is:* Use the best tool available (currently Claude for PRDs)* Invest in improving your prompting skills ideally with as much original and insightful human, company aware, and context aware thinking as possible.Real-World Workflows: How to Actually Use This in Your Day-to-Day PM WorkTheory is great. Here's how to incorporate these insights into your actual product management workflows.The Weekly Sprint Planning WorkflowEvery PM I know spends hours each week preparing for sprint planning. You need to refine user stories, clarify acceptance criteria, anticipate engineering questions, and align with design and data science. AI can compress this work significantly.Here's an example workflow:Monday morning (30 minutes):* Review upcoming priorities and open your rough notes/outline in Google Docs* Open Claude and paste your outline with this prompt:“I'm preparing for sprint planning. Based on these priorities [paste notes], generate detailed user stories with acceptance criteria. Format each as: User story, Business context, Technical considerations, Acceptance criteria, Dependencies, Open questions.”Monday afternoon (20 minutes):* Review Claude's output critically* Identify gaps, unclear requirements, or missing context* Follow up with targeted prompts:“The user story about authentication is too vague. Break it down into separate stories for: social login, email/password, session management, and password reset. For each, specify security requirements and edge cases.”Tuesday morning (15 minutes):* Generate mockups for any UI-heavy stories:“Create an HTML mockup for the login flow showing: landing page, social login options, email/password form, error states, and success redirect.”* Even if the HTML doesn't work perfectly, it gives your designers a starting pointBefore sprint planning (10 minutes):* Ask Claude to anticipate engineering questions:“Review these user stories as if you're a senior engineer. What questions would you ask? What concerns would you raise about technical feasibility, dependencies, or edge cases?”* This preparation makes you look thoughtful and helps the meeting run smoothlyTotal time investment: ~75 minutes. Typical time saved: 3-4 hours compared to doing this manually.The Stakeholder Alignment WorkflowGetting alignment from multiple stakeholders (product leadership, engineering, design, data science, legal, marketing) is one of the hardest parts of PM work. AI can help you think through different stakeholder perspectives and craft compelling communications for each.Here's how:Step 1: Map your stakeholders (10 minutes)Create a quick table in a doc:Stakeholder | Primary Concern | Decision Criteria | Likely Objections VP Product | Strategic fit, ROI | Company OKRs, market opportunity | Resource allocation vs other priorities VP Eng | Technical risk, capacity | Engineering capacity, tech debt | Complexity, unclear requirements Design Lead | User experience | User research, design principles | Timeline doesn't allow proper design process Legal | Compliance, risk | Regulatory requirements | Data privacy, user consent flowsStep 2: Generate stakeholder-specific communications (20 minutes)For each key stakeholder, ask Claude:“I need to pitch this product idea to [Stakeholder]. Based on this PRD, create a 1-page brief addressing their primary concern of [concern from your table]. Open with the specific value for them, address their likely objection of [objection], and close with a clear ask. Tone should be [professional/technical/strategic] based on their role.”Then you'll have customized one-pagers for your pre-meetings with each stakeholder, dramatically increasing your alignment rate.Step 3: Synthesize feedback (15 minutes)After gathering stakeholder input, ask Claude to help you synthesize:“I got the following feedback from stakeholders: [paste feedback]. Identify: (1) Common themes, (2) Conflicting requirements, (3) Legitimate concerns vs organizational politics, (4) Recommended compromises that might satisfy multiple parties.”This pattern-matching across stakeholder feedback is something AI does really well and saves you hours of mental processing.The Quarterly Planning WorkflowQuarterly or annual planning is where product strategy gets real. You need to synthesize market trends, customer feedback, technical capabilities, and business objectives into a coherent roadmap. AI can accelerate this dramatically.Six weeks before planning:* Start collecting input (customer interviews, market research, competitive analysis, engineering feedback)* Don't wait until the last minuteFour weeks before planning:Dump everything into Claude with this structure:“I'm creating our Q2 roadmap. Context:* Business objectives: [paste from leadership]* Customer feedback themes: [paste synthesis]* Technical capabilities/constraints: [paste from engineering]* Competitive landscape: [paste analysis]* Current product gaps: [paste from your analysis]Generate 5 strategic themes that could anchor our Q2 roadmap. For each theme:* Strategic rationale (how it connects to business objectives)* Key initiatives (2-3 major features/projects)* Success metrics* Resource requirements (rough estimate)* Risks and mitigations* Customer segments addressed”This gives you a strategic framework to react to rather than starting from a blank page.Three weeks before planning:Iterate on the most promising themes:“Deep dive on Theme 3. Generate:* Detailed initiative breakdown* Dependencies on platform/infrastructure* Phasing options (MVP vs full build)* Go-to-market considerations* Data requirements* Open questions requiring research”Two weeks before planning:Pressure-test your thinking:“Play devil's advocate on this roadmap. What are the strongest arguments against each initiative? What am I likely missing? What failure modes should I plan for?”This adversarial prompting forces you to strengthen weak points before your leadership reviews it.One week before planning:Generate your presentation:“Create an executive presentation for this roadmap. Structure: (1) Market context and strategic imperative, (2) Q2 themes and initiatives, (3) Expected outcomes and metrics, (4) Resource requirements, (5) Key risks and mitigations, (6) Success criteria for decision. Make it compelling but data-driven. Tone: confident but not overselling.”Then add your company-specific context, visual brand, and personal voice.The Customer Research WorkflowAI can't replace talking to customers, but it can help you prepare better questions, analyze feedback more systematically, and identify patterns faster.Before customer interviews:“I'm interviewing customers about [topic]. Generate:* 10 open-ended questions that avoid leading the witness* 5 follow-up questions for each main question* Common cognitive biases I should watch for* A framework for categorizing responses”This prep work helps you conduct better interviews.After interviews:“I conducted 15 customer interviews. Here are the key quotes: [paste anonymized quotes]. Identify:* Recurring themes and patterns* Surprising insights that contradict our assumptions* Segments with different needs* Implied needs customers didn't articulate directly* Recommended next steps for validation”AI is excellent at pattern-matching across qualitative data at scale.The Crisis Management WorkflowSomething broke. The site is down. Data was lost. A feature shipped with a critical bug. You need to move fast.Immediate response (5 minutes):“Critical incident. Details: [brief description]. Generate:* Incident classification (Sev 1-4)* Immediate stakeholders to notify* Draft customer communication (honest, apologetic, specific about what happened and what we're doing)* Draft internal communication for leadership* Key questions to ask engineering during investigation”Having these drafted in 5 minutes lets you focus on coordination and decision-making rather than wordsmithing.Post-incident (30 minutes):“Write a post-mortem based on this incident timeline: [paste timeline]. Include:* What happened (technical details)* Root cause analysis* Impact quantification (users affected, revenue impact, time to resolution)* What went well in our response* What could have been better* Specific action items with owners and deadlines* Process changes to prevent recurrence Tone: Blameless, focused on learning and improvement.”This gives you a strong first draft to refine with your team.Common Pitfalls: What Not to Do with AI in Product ManagementNow let's talk about the mistakes I see PMs making with AI tools. Pitfall #1: Treating AI Output as FinalThe biggest mistake is copy-pasting AI output directly into your PRD, roadmap presentation, or stakeholder email without critical review.The result? Documents that are grammatically perfect but strategically shallow. Presentations that sound impressive but don't hold up under questioning. Emails that are professionally worded but miss the subtext of organizational politics.The fix: Always ask yourself:* Does this reflect my actual strategic thinking, or generic best practices?* Would my CEO/engineering lead/biggest customer find this compelling and specific?* Are there company-specific details, customer insights, or technical constraints that only I know?* Does this sound like me, or like a robot?Add those elements. That's where your value as a PM comes through.Pitfall #2: Using AI as a Crutch Instead of a ToolSome PMs use AI because they don't want to think deeply about the product. They're looking for AI to do the hard work of strategy, prioritization, and trade-off analysis.This never works. AI can help you think more systematically, but it can't replace thinking.If you find yourself using AI to avoid wrestling with hard questions (”Should we build X or Y?” “What's our actual competitive advantage?” “Why would customers switch from the incumbent?”), you're using it wrong.The fix: Use AI to explore options, not to make decisions. Generate three alternatives, pressure-test each one, then use your judgment to decide. The AI can help you think through implications, but you're still the one choosing.Pitfall #3: Not IteratingGetting mediocre AI output and just accepting it is a waste of the technology's potential.The PMs who get exceptional results from AI are the ones who iterate. They generate an initial response, identify what's weak or missing, and ask follow-up questions. They might go through 5-10 iterations on a key section of a PRD.Each iteration is quick (30 seconds to type a follow-up prompt, 30 seconds to read the response), but the cumulative effect is dramatically better output.The fix: Budget time for iteration. Don't try to generate a complete, polished PRD in one prompt. Instead, generate a rough draft, then spend 30 minutes iterating on specific sections that matter most.Pitfall #4: Ignoring the Political and Human ContextAI tools have no understanding of organizational politics, interpersonal relationships, or the specific humans you're working with.They don't know that your VP of Engineering is burned out and skeptical of any new initiatives. They don't know that your CEO has a personal obsession with a specific competitor. They don't know that your lead designer is sensitive about not being included early enough in the process.If you use AI-generated communications without layering in this human context, you'll create perfectly worded documents that land badly because they miss the subtext.The fix: After generating AI content, explicitly ask yourself: “What human context am I missing? What relationships do I need to consider? What political dynamics are in play?” Then modify the AI output accordingly.Pitfall #5: Over-Relying on a Single ToolDifferent AI tools have different strengths. Claude is great for strategic depth, ChatPRD is great for structure, Gemini integrates well with Google Workspace.If you only ever use one tool, you're missing opportunities to leverage different strengths for different tasks.The fix: Keep 2-3 tools in your toolkit. Use Claude for important PRDs and strategic documents. Use Gemini for quick internal documentation that needs to integrate with Google Docs. Use ChatPRD when you want more guided structure. Match the tool to the task.Pitfall #6: Not Fact-Checking AI OutputAI tools hallucinate. They make up statistics, misrepresent competitors, and confidently state things that aren't true. If you include those hallucinations in a PRD that goes to leadership, you look incompetent.The fix: Fact-check everything, especially:* Statistics and market data* Competitive feature claims* Technical capabilities and limitations* Regulatory and compliance requirementsIf the AI cites a number or makes a factual claim, verify it independently before including it in your document.The Meta-Skill: Prompt Engineering for PMsLet's zoom out and talk about the underlying skill that makes all of this work: prompt engineering.This is a real skill. The difference between a mediocre prompt and a great prompt can be 10x difference in output quality. And unlike coding or design, where there's a steep learning curve, prompt engineering is something you can get good at quickly.Principle 1: Provide Context Before InstructionsBad prompt:“Write a PRD for an AI tutor”Good prompt:“I'm a PM at an edtech company with 2M users, primarily high school students. We're exploring an AI tutor feature to complement our existing video content library and practice problems. Our main competitors are Khan Academy and Course Hero. Our differentiation is personalized learning paths based on student performance data.Write a PRD for an AI tutor feature targeting students in the middle 80% academically who struggle with science and math.”The second prompt gives Claude the context it needs to generate something specific and strategic rather than generic.Principle 2: Specify Format and ConstraintsBad prompt:“Generate success metrics”Good prompt:“Generate 5-7 success metrics for this feature. Include a mix of:* Leading indicators (early signals of success)* Lagging indicators (definitive success measures)* User behavior metrics* Business impact metricsFor each metric, specify: name, definition, target value, measurement method, and why it matters.”The structure you provide shapes the structure you get back.Principle 3: Ask for Multiple OptionsBad prompt:“What should our Q2 priorities be?”Good prompt:“Generate 3 different strategic approaches for Q2:* Option A: Focus on user acquisition* Option B: Focus on engagement and retention* Option C: Focus on monetizationFor each option, detail: key initiatives, expected outcomes, resource requirements, risks, and recommendation for or against.”Asking for multiple options forces the AI (and forces you) to think through trade-offs systematically.Principle 4: Specify Audience and ToneBad prompt:“Summarize this PRD”Good prompt:“Create a 1-paragraph summary of this PRD for our skeptical VP of Engineering. Tone: Technical, concise, addresses engineering concerns upfront. Focus on: technical architecture, resource requirements, risks, and expected engineering effort. Avoid marketing language.”The audience and tone specification ensures the output will actually work for your intended use.Principle 5: Use Iterative RefinementDon't try to get perfect output in one prompt. Instead:First prompt: Generate rough draft Second prompt: “This is too generic. Add specific examples from [our company context].” Third prompt: “The technical section is weak. Expand with architecture details and dependencies.” Fourth prompt: “Good. Now make it 30% more concise while keeping the key details.”Each iteration improves the output incrementally.Let me break down the prompting approach that worked in this experiment, because this is immediately actionable for your work tomorrow.Strategy 1: The Structured Outline ApproachDon't go from zero to full PRD in one prompt. Instead:* Start with strategic thinking - Spend 10-15 minutes outlining why you're building this, who it's for, and what problem it solves* Get specific - Don't say “users,” say “high school students in the middle 80% of academic performance”* Include constraints - Budget, timeline, technical limitations, competitive landscape* Dump your outline into the AI - Now ask it to expand into a full PRD* Iterate section by section - Don't try to perfect everything at onceThis is exactly what I did in my experiment, and even with my somewhat sloppy outline, the results were dramatically better than they would have been with a single-sentence prompt.Strategy 2: The Comparative Analysis PatternOne technique I used that worked particularly well: asking each tool to do the same specific task and comparing results.For example, I asked all five tools: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This forced each tool to synthesize the entire PRD into a compelling pitch while accounting for a specific, challenging audience. The variation in quality was revealing—and it gave me multiple options to choose from or blend together.Actionable tip: When you need something critical (a pitch, an executive summary, a key decision framework), generate it with 2-3 different AI tools and take the best elements from each. This “ensemble approach” often produces better results than any single tool.Strategy 3: The Iterative Refinement LoopDon't treat the AI output as final. Use it as a first draft that you then refine through conversation with the AI.After getting the initial PRD, I could have asked follow-up questions like:* “What's missing from this PRD?”* “How would you strengthen the success metrics section?”* “Generate 3 alternative approaches to the core feature set”Each iteration improves the output and, more importantly, forces me to think more deeply about the product.What This Means for Your CareerIf you're an early or mid-career PM reading this, you might be thinking: “Great, so AI can write PRDs now. Am I becoming obsolete?”Absolutely not. But your role is evolving, and understanding that evolution is critical.The PMs who will thrive in the AI era are those who:* Excel at strategic thinking - AI can generate options, but you need to know which options align with company strategy, customer needs, and technical feasibility* Master the art of prompting - This is a genuine skill that separates mediocre AI users from exceptional ones* Know when to use AI and when not to - Some aspects of product work benefit enormously from AI. Others (user interviews, stakeholder negotiation, cross-functional relationship building) require human judgment and empathy* Can evaluate AI output critically - You need to spot the hallucinations, the generic fluff, and the strategic misalignments that AI inevitably producesThink of AI tools as incredibly capable interns. They can produce impressive work quickly, but they need direction, oversight, and strategic guidance. Your job is to provide that guidance while leveraging their speed and breadth.The Real-World Application: What to Do Monday MorningLet's get tactical. Here's exactly how to apply these insights to your actual product work:For Your Next PRD:* Block 30 minutes for strategic thinking - Write your back-of-the-napkin outline in Google Docs or your tool of choice* Open Claude (or ChatPRD if you want more structure)* Copy your outline with this prompt:“I'm a product manager at [company] working on [product area]. I need to create a comprehensive PRD based on this outline. Please expand this into a complete PRD with the following sections: [list your preferred sections]. Make it detailed enough for engineering to start breaking down into user stories, but concise enough for leadership to read in 15 minutes. [Paste your outline]”* Review the output critically - Look for generic statements, missing details, or strategic misalignments* Iterate on specific sections:“The success metrics section is too vague. Please provide 3-5 specific, measurable KPIs with target values and explanation of why these metrics matter.”* Generate supporting materials:“Create a visual mockup of the core user flow showing the key interaction points.”* Synthesize the best elements - Don't just copy-paste the AI output. Use it as raw material that you shape into your final documentFor Stakeholder Communication:When you need to pitch something to leadership or engineering:* Generate 3 versions of your pitch using different tools (Claude, ChatPRD, and one other)* Compare them for:* Clarity and conciseness* Strategic framing* Compelling value proposition* Addressing likely objections* Blend the best elements into your final version* Add your personal voice - This is crucial. AI output often lacks personality and specific company context. Add that yourself.For Feature Prioritization:AI tools can help you think through trade-offs more systematically:“I'm deciding between three features for our next release: [Feature A], [Feature B], and [Feature C]. For each feature, analyze: (1) Estimated engineering effort, (2) Expected user impact, (3) Strategic alignment with making our platform the go-to solution for [your market], (4) Risk factors. Then recommend a prioritization with rationale.”This doesn't replace your judgment, but it forces you to think through each dimension systematically and often surfaces considerations you hadn't thought of.The Uncomfortable Truth About AI and Product ManagementLet me be direct about something that makes many PMs uncomfortable: AI will make some PM skills less valuable while making others more valuable.Less valuable:* Writing boilerplate documentation* Creating standard frameworks and templates* Generating routine status updates* Synthesizing information from existing sourcesMore valuable:* Strategic product vision and roadmapping* Deep customer empathy and insight generation* Cross-functional leadership and influence* Critical evaluation of options and trade-offs* Creative problem-solving for novel situationsIf your PM role primarily involves the first category of tasks, you should be concerned. But if you're focused on the second category while leveraging AI for the first, you're going to be exponentially more effective than your peers who resist these tools.The PMs I see succeeding aren't those who can write the best PRD manually. They're those who can write the best PRD with AI assistance in one-tenth the time, then use the saved time to talk to more customers, think more deeply about strategy, and build stronger cross-functional relationships.Advanced Techniques: Beyond Basic PRD GenerationOnce you've mastered the basics, here are some advanced applications I've found valuable:Competitive Analysis at Scale“Research our top 5 competitors in [market]. For each one, analyze: their core value proposition, key features, pricing strategy, target customer, and likely product roadmap based on recent releases and job postings. Create a comparison matrix showing where we have advantages and gaps.”Then use web search tools in Claude or Perplexity to fact-check and expand the analysis.Scenario Planning“We're considering three strategic directions for our product: [Direction A], [Direction B], [Direction C]. For each direction, map out: likely customer adoption curve, required technical investments, competitive positioning in 12 months, and potential pivots if the hypothesis proves wrong. Then identify the highest-risk assumptions we should test first for each direction.”This kind of structured scenario thinking is exactly what AI excels at—generating multiple well-reasoned perspectives quickly.User Story GenerationAfter your PRD is solid:“Based on this PRD, generate a complete set of user stories following the format ‘As a [user type], I want to [action] so that [benefit].' Include acceptance criteria for each story. Organize them into epics by functional area.”This can save your engineering team hours of grooming meetings.The Tools Will Keep Evolving. Your Process Shouldn'tHere's something important to remember: by the time you read this, the specific rankings might have shifted. Maybe ChatGPT-5 has leapfrogged Claude. Maybe a new specialized tool has emerged.But the core principles won't change:* Do strategic thinking before touching AI* Use the best tool available for your specific task* Iterate and refine rather than accepting first outputs* Blend AI capabilities with human judgment* Focus your time on the uniquely human aspects of product managementThe specific tools matter less than your process for using them effectively.A Final Experiment: The Skeptical VP TestI want to share one more insight from my testing that I think is particularly relevant for early and mid-career PMs.Toward the end of my experiment, I gave each tool this prompt: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This is such a realistic scenario. How many times have you needed to pitch an idea to a skeptical technical leader via Slack or email? Someone who's brilliant, who's seen a thousand product ideas fail, and who can spot b******t from a mile away?The quality variation in the responses was fascinating. ChatGPT gave me something that felt generic and safe. Gemini was better but still a bit too enthusiastic. Grok was... well, Grok.But Claude and ChatPRD both produced messages that felt authentic, technically credible, and appropriately confident without being overselling. They acknowledged the engineering challenges while framing the opportunity compellingly.The lesson: When the stakes are high and the audience is sophisticated, the quality of your AI tool matters even more. That skeptical VP can tell the difference between a carefully crafted message and AI-generated fluff. So can your CEO. So can your biggest customers.Use the best tools available, but more importantly, always add your own strategic thinking and authentic voice on top.Questions to Consider: A Framework for Your Own ExperimentsAs I wrapped up my Loom, I posed some questions to the audience that I'll pose to you:“Let me know in the comments, if you do your PRDs using AI differently, do you start with back of the envelope? Do you say, oh no, I just start with one sentence, and then I let the chatbot refine it with me? Or do you go way more detailed and then use the chatbot to kind of pressure test it?”These aren't rhetorical questions. Your answer reveals your approach to AI-augmented product work, and different approaches work for different people and contexts.For early-career PMs: I'd recommend starting with more detailed outlines. The discipline of thinking through your product strategy before touching AI will make you a stronger PM. You can always compress that process later as you get more experienced.For mid-career PMs: Experiment with different approaches for different types of documents. Maybe you do detailed outlines for major feature PRDs but use more iterative AI-assisted refinement for smaller features or updates. Find what optimizes your personal productivity while maintaining quality.For senior PMs and product leaders: Consider how AI changes what you should expect from your PM team. Should you be reviewing more AI-generated first drafts and spending more time on strategic guidance? Should you be training your team on effective AI usage? These are leadership questions worth grappling with.The Path Forward: Continuous ExperimentationMy experiment with these five AI tools took 45 minutes. But I'm not done experimenting.The field of AI-assisted product management is evolving rapidly. New tools launch monthly. Existing tools get smarter weekly. Prompting techniques that work today might be obsolete in three months.Your job, if you want to stay at the forefront of product management, is to continuously experiment. Try new tools. Share what works with your peers. Build a personal knowledge base of effective prompts and workflows. And be generous with what you learn. The PM community gets stronger when we share insights rather than hoarding them.That's why I created this Loom and why I'm writing this post. Not because I have all the answers, but because I'm figuring it out in real-time and want to share the journey.A Personal Note on Coaching and ConsultingIf this kind of practical advice resonates with you, I'm happy to work with you directly.Through my pm coaching practice, I offer 1:1 executive, career, and product coaching for PMs and product leaders. We can dig into your specific challenges: whether that's leveling up your AI workflows, navigating a career transition, or developing your strategic product thinking.I also work with companies (usually startups or incubation teams) on product strategy, helping teams figure out PMF for new explorations and improving their product management function.The format is flexible. Some clients want ongoing coaching, others prefer project-based consulting, and some just want a strategic sounding board for a specific decision. Whatever works for you.Reach out through tomleungcoaching.com if you're interested in working together.OK. Enough pontificating. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Arthur Breitman, the co-founder of Tezos, is a computer scientist and entrepreneur. Arthur has a background in mathematics and computer science, and prior to the Tezos project, he worked in quantitative finance at Goldman Sachs and Morgan Stanley, and as a research engineer at Google and Waymo. Arthur graduated from the École Polytechnique and the Courant Institute of NYU where he studied applied mathematics. Arthur is a member of the Tezos Foundation Council and is also a director at Trilitech, a London-based adoption team for the Tezos blockchain. In this conversation, we discuss:- Tokenized uranium - Quantum's threat to Bitcoin - Deep dive on Tezos blockchain - Proof-of-stake is the best consensus - There is no PMF for security on blockchains - The emergence of Tezos as the artists' blockchain - EVM compatibility layer - 19 upgrades without a hard fork - The Data Availability Layer deep dive - The future of Tezos TezosX: @tezosWebsite: tezos.comLinkedIn: TezosArthur BreitmanX: @ArthurBLinkedIn: Arthur Breitman---------------------------------------------------------------------------------This episode is brought to you by PrimeXBT.PrimeXBT offers a robust trading system for both beginners and professional traders that demand highly reliable market data and performance. Traders of all experience levels can easily design and customize layouts and widgets to best fit their trading style. PrimeXBT is always offering innovative products and professional trading conditions to all customers.  PrimeXBT is running an exclusive promotion for listeners of the podcast. After making your first deposit, 50% of that first deposit will be credited to your account as a bonus that can be used as additional collateral to open positions. Code: CRYPTONEWS50 This promotion is available for a month after activation. Click the link below: PrimeXBT x CRYPTONEWS50FollowApple PodcastsSpotifyAmazon MusicRSS FeedSee All

Most conversations in startups begin at zero: what's the idea, who's the customer, how big is the market. But the stage before that, when you know you're ready to be a founder yet the direction is still completely undefined. That strange, uncomfortable, high-potential zone Aditya Agarwal calls “minus one.”In this episode, Aditya and Prateek Mehta breaks down what happens in this “figuring out” stage. The questions people avoid, the habits that matter, and why some of the best companies begin long before their founders have any conviction.We get into how this stage is evolving in the AI era. Exploration cycles are faster, technical founders can test more directions than ever, and the gap between “I'm experimenting” and “I'm running a real company” has narrowed. India's builder ecosystem is shifting too: more second-time founders, more people with real outcomes behind them, and far more comfort sitting with ambiguity.Aditya shares his own minus-one moment after Facebook, his startup acquisition, Dropbox's IPO, and Flipkart, and why that transitional period changed the way he thinks about early-stage startups. Prateek brings on-the-ground view from Bangalore, where ambition, technical depth, and the appetite to explore hard problems from robotics to voice models to AI infra are rising.This episode is for anyone who feels they're between missions. Anyone who wants to understand why the most important part of building a company might actually be the time you spend before you even know what you're building.00:00- Trailer01:06- Aditya's journey to starting SPC after Facebook & Dropbox 03:48- A “learning club” for people in figuring-out stage06:23- 3 Northstars of the SPC community07:02- How SPC evolved from a community to a fund10:32- Not everyone should be a founder11:51- 1% selection rate13:53- Building conviction in 1 of 3 outcomes16:36- SPC is at PMF stage18:38- Mismatch of traditional VC's v/s rapid pace startups19:04- How AI has impacted investing at SPC26:32- How AI has changed VC firms29:02- Axis of curiosity replacing thesis30:17- Star Companies of SPC US33:34- Binny Bansal's role in starting SPC India37:16- Questions & confusions as founders in early stage39:50- Number of great entrepreneurs is NOT small41:49- Talent density in India vs Bay Area44:04- Founders don't need a culture of permission45:08- India tier 2 and 3 does invest heavily in AI46:11- AI is truly democratizing tech49:09- Math gives India advantage in AI51:48- A lot of science fiction is coming true-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Zaid Rahman is the Co-founder and CEO of Flex.Flex is the AI native private bank for high net worth middle market business owners, headlined by it's 60-day interest free credit card for businesses.Flex just announced their $60 million Series B, as well as their new consumer product, Flex Elite, which pits it head-to-head against Amex for the consumer spending of some of the wealthiest people in America. It's products now spans from when a business owner first generates revenue, all the way to when they spend that cash personally.This conversation goes inside how the company scaled from zero to a $70 million revenue run rate in two years, and everything Zaid learned along the way.Thank you to Eric Bahn at Hustle Fund, Jeff Morris Jr. at Chapter One, Andrew Ziperski at General Catalyst, and Jared Thomas and Ewan Steel at Flex for helping brainstorming topics for the conversation.Timestamps:(1:44) Raising $60m to fix business finance(3:23) Flex Elite: Personal + Business banking(4:48) Jumbo shrimps: powering 40% of US payroll(9:16) The forgotten mid market business(14:01) “Flex fuels ambition”(16:08) How to serve entrepreneurs in middle America(22:58) Flex's 5-pillar product suite(27:12) Starting Flex to help construction companies(31:51) Using AI to lend to mid-market customers(40:22) Power of multi-product in fintech(43:53) Zero to $3B in volume in 18 months(44:43) Raising a bear market Series A in 2023(51:00) How referrals landed their first big customers(55:07) Flex's playbook for 85% organic growth(1:01:15) Dissecting various accents(1:04:22) Building a quiet luxury brand(1:09:33) Importance of customer happiness(1:12:43) Why CEO's should be the top sales person(1:13:58) Building lots of in-house software(1:24:33) PMF is like operating a popular restaurant(1:30:49) How to raise a debt facility(1:34:48) Recruiting is so crucial for startups(1:39:00) Why VC's hate lending businesses(1:45:14) Underserved vs Underbanked in fintech(1:48:02) Why business owners want personal + business banking(1:54:49) Acquiring Maza, leaning in to M&A(2:02:53) Most fintech companies look the same(2:08:35) Founder group therapy with Eric at Hustle Fund(2:11:50) The Thiel Fellowship's 10% unicorn hit rate(2:15:52) Lesson from the ruler of Dubai(2:19:24) Building Flex's risk underwriting engine(2:26:58) Flex's AI opportunityReferencedTry Flex: https://www.flex.oneCareers at Flex: https://jobs.lever.co/Flex/Basel III https://en.wikipedia.org/wiki/Basel_IIILinguistic TikTok account: https://www.tiktok.com/@zaydupreeLazy luxury: most worn shoes on private jets: https://www.wsj.com/style/fashion/lazy-luxury-sneakers-are-these-the-most-worn-shoes-on-private-jets-7801be30Follow ZaidTwitter: https://x.com/zaidrmnLinkedIn: https://www.linkedin.com/in/zaidrahmanFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

This week, we're joined by Ishan Gupta, co-founder of Juicebox — a company redefining how recruiting works in an AI-native world.Ishan shares how they pivoted from earlier ideas to focus on the highest value part of hiring: identifying the right people and getting them into process. With Juicebox, you describe what you want in natural language and the platform searches more than 800 million professional profiles, surfaces the best matches, and engages candidates through recruiting agents.Their breakout feature, Autopilot, uses LLMs to semantically evaluate and stack rank candidates based on nuanced criteria, which quickly drove organic growth and strong PMF. We talk about why their data makes the product so sticky, how they see recruiting evolving over the next five years, what led them to raise their Series A, and the culture they are building around moving fast and being intellectually honest.Ishan also shares what they are building next with their memory layer, which will make it clear what the AI is learning over time and how it improves future searches.Episode chapters:1:44 - Competitive programming4:35 - Choosing the company name5:55 - YC and the pivot8:00 - How Juicebox differs from traditional recruiting9:51 - The killer feature14:30 - What makes the product sticky17:15 - Is recruiting a zero sum game19:10 - The macro view of hiring22:05 - Raising a later Series A24:10 - Product expansion26:45 - Quick fire round This episode is brought to you by Grata, the leading deal sourcing platform for private equity. Grata's AI powered search, investment grade data, and intuitive workflows help you find and win the right deals faster. Visit grata.com to book a demo.This episode is also sponsored by Overlap, the AI powered app that uses LLMs to surface the best moments from any podcast. Overlap reads full transcripts, finds the most relevant clips, and stitches them into a personalized stream of insights. Tap into podcasts as a real information source with Overlap 2.0, now available on the App Store.

Brian Balfour, Founder & CEO of Reforge and former VP of Growth at HubSpot, joins Mostly Growth to explore why product-market fit is a moving target. He introduces the concept of the Product-Market Fit Treadmill, a state where rising customer expectations and competitive pressure make it harder than ever to stay ahead. Brian breaks down how AI has accelerated PMF collapse, explains the hidden costs of product adoption, and shares how Reforge shipped five AI-native products with a team of just 20 people. Packed with frameworks, strategic insight, and startup realism, this episode is essential listening for product leaders, operators, and founders navigating the next wave of GTM.—SPONSORS:Pulley is the cap table management platform built for CFOs and finance leaders who need reliable, audit-ready data and intuitive workflows, without the hidden fees or unreliable support. Switch in as little as 5 days and get 25% off your first year: https://pulley.com/mostlymetricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.com—LINKS:Mostly Metrics: https://www.mostlymetrics.comCJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Growth Unhinged: https://www.growthunhinged.com/Kyle on LinkedIn: https://www.linkedin.com/in/kyle-poyar/Brian Balfour: brianbalfour.comBrian on LinkedIn: https://www.linkedin.com/in/bbalfour/Slacker Stuff: https://www.slackerstuff.com/Ben on LinkedIn: https://www.linkedin.com/in/slackerstuff/https://brianbalfour.com/four-fits-growth-frameworkhttps://x.com/amasad/status/1981201454032703662?s=46https://getlatka.com/companies/firefliesaihttps://x.com/rowancheung/status/1988218743952916537?https://gamma.app/insights/how-we-built-a-usd100m-business-differently—RELATED EPISODES:When the marketing math doesn't math | with Emily Kramerhttps://youtu.be/sSuoV_YSrlwWhy Founders Are Posting Sad Dinnershttps://youtu.be/Zl6NSIHF2Gk—TIMESTAMPS:00:00:00 Preview and Intro00:01:51 Sponsors – Pulley, Metronome00:04:11 Introducing Brian Balfour & Reforge background00:07:22 Evergreen frameworks & Four Fits resurgence00:11:01 PMF treadmill and rising expectations00:14:26 AI shocks and PMF collapse (Chegg)00:16:43 CRM expectations & AI-native workflows00:20:44 R&D as ongoing cost to serve00:22:26 Customers buying based on future product velocity00:24:32 Communicating rapid releases & driving adoption00:25:17 Reforge's expanding AI product suite00:27:52 Product delivery vs. product adoption bottlenecks00:29:32 Platform distribution shifts introduction00:30:51 Evaluating emerging platforms00:32:04 The open → close platform cycle00:33:31 Moats, escape velocity & platform dominance00:36:32 Choosing major vs. emerging platforms00:40:22 ChatGPT dominance in AI discovery00:42:16 Hiring, resumes & filtering AI-generated applications00:43:30 AI note-taking market & “Flintstoning”00:47:03 Trying Gamma & AI-generated presentation tools00:50:08 AI onboarding innovations (WhatsApp agent)#MostlyGrowthPodcast #ProductMarketFit #BrianBalfour #StartupStrategy #Reforge This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

Rahul Vohra is the Founder and CEO of Superhuman Mail.Rahul sold his company to Grammarly in July of 2025, which had just acquired Coda in 2024. Following the acquisitions, the combined companies rebranded to Superhuman in October of 2025.And it's quietly one of the most underrated businesses that no one is talking about, with over $700 million ARR and 40 million Daily Active Users. Grammarly spent 15+ years building integrations with over a million other products, that they're now layering more AI products on top of.We talk about Rahul's journey building Superhuman, go inside the acquisition, all the lessons he's learned from selling two companies, why you should design your product like a video game, and we also re-visit his famous quantitative guide to finding PMF.Thanks to Todd Goldberg, Ed Sim, Shomik Ghosh, Ryan Hoover, and Rahul's brother Gaurav Vohra for helping brainstorm topics for this conversation.Thank you to Hanover Park for supporting this episode! Upgrade your fund admin to the 21st century https://www.hanoverpark.com/TurnerTimestamps:(2:42) Inside the Superhuman acquisition(11:09) Grammarly: $700M ARR, 40M DAUs(18:53) How to sequence your product roadmap(24:43) Vision for the new Superhuman(32:43) Build your product like a video game(38:24) Designing Karamja island in Runescape(41:10) Build products like toys and games(44:53) Starting a Machine Learning PhD in 2006(48:49) Dropping out to start his first company(50:47) Rapportive's crazy accidental launch(57:56) Meeting Superhuman co-founders(1:02:17) Being 1 of 20 to access LinkedIn's API(1:06:38) Almost getting acquired by LinkedIn(1:10:32) Nearly dieing, getting acquired with 2 weeks of runway(1:20:08) Diligence from VCs vs Acquirers(1:26:37) Rahul's quantitative framework for PMF(1:30:45) How to build an enduring brand(1:31:51) Rahul's AI-powered productivity stack(1:35:01) Todd and Rahul's angel fund(1:36:45) We need more solo foundersReferencedSuperhuman: https://www.superhuman.comGrammarly: https://www.grammarly.comHigh Resolution Fundraising: https://paulgraham.com/hiresfund.htmlHigh Resolution Fundraising: https://paulgraham.com/hiresfund.htmlSuperhuman Quantitative Framework for Finding PMF: https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit/Whisper Flow: https://wisprflow.aiFollow RahulTwitter: https://x.com/rahulvohraLinkedIn: https://www.linkedin.com/in/rahulvohra/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

How to Find Product-Market Fit (Without Fooling Yourself) Founders love to sprint. Markets prefer you walk, listen, and maybe ask a few humans what they actually want. In this episode, product leader and startup advisor Shweta Agrawal joins Rina to break down why so many early-stage teams skip the fundamentals  (customer discovery, focus, ICP clarity) and pay the price later. From health-tech to ed-tech to “AI for everyone” tools, Shweta shares raw stories, red flags, and the simple practices that separate wishful thinking from real traction. Key Topics Discussed in This Episode Why Founders Keep Skipping Customer Discovery Shweta explains why “I know the market because I am the market” is the fastest path to building something no one pays for — and how to break that pattern early. Product-Market Fit Signals That Actually Matter Renewals, retention, usage depth, early-adopter behavior — and why the infamous “40% Would Be Very Disappointed” test still slaps in 2025. When (and Whether) You're Ready for Go-to-Market The building blocks founders overlook: ICP focus, positioning by region, partners who actually have reach, and the risks of going global before you even have 20 happy users.  Why Listen to This Episode? In this thought-provoking episode, you'll gain: A reality check on the biggest founder blind spots (and how to avoid them). A practical walkthrough of PMF signals that aren't vanity metrics. A step-by-step look at launching intentionally — not reactively. Lessons on adapting your product for different regions, cultures, and buying habits. Whether you're a PM or a founder, this episode will recalibrate your instincts and help you build with confidence, not guesswork.  Related Resources Check out these additional tools and resources to add to your PM belt: Productside Resource Library More Productside Stories Podcast Episodes Explore Productside Courses 

Early-stage founders often claim they've reached product market fit, but when you look closer, it's usually built on vibes, not data. In this episode of In Demand, Asia and Kim unpack what real product market fit looks like, how to measure it quantitatively, and why most early-stage SaaS companies are too quick to assume they've found it.  If you've ever wondered how to know when you've actually hit product market fit, or if you might be fooling yourself, this episode gives you the frameworks and numbers to tell the difference. Got a question you'd like Asia to unpack on the podcast? Record a voicemail here. Links:  DemandMaven Previous In Demand Episodes that discuss NRR: episode 46 and episode 37 Superhuman Product Market Fit Survey ProfitWell Chart Mogul Chapters (00:02:20) - What is product market fit, and how was it historically measured?(00:07:00) - The product market fit survey and its limitations.(00:11:30) - Gross customer retention (GCR) as an underrated metric for measuring product market fit.(00:16:00) - Net Revenue Retention (NRR) as a deeper sign of product-market alignment.(00:20:10) - How GCR and NRR tell different parts of the story.(00:26:05) - Secondary indicators: churn rate, close rate, and trial-to-paid conversions.(00:28:05) - Why cohorting/segmenting reveals where PMF actually exists.(00:34:50) - You might have PMF for one segment but not another.(00:36:45) - The cautionary tale of assuming PMF too soon and how DemandMaven sets expectations with new clients.(00:44:30) - The reality check: if you've never charged customers, you don't have PMF.

It's Etiquette Finishing School Day at Slow Ventures, Sam dials in from the Four Seasons in a Brioni suit to recap Slow's first-ever Etiquette School—covering caviar bumps, sommelier tips, and the “low heart rate” approach to leadership. The crew argue that etiquette now matters in tech because trust is scarce and “PMF-only” is an outdated YC-era story. Jess also unpacks details from Apple's Gemini deal, Sequoia's leadership shuffle, Anthropic's latest numbers, and crypto's meme-driven chaos. Watch till the end for free No Kings and Queens of Corbet protest tees from Sam.Chapters:02:33 Etiquette Day at Slow — Sam's recap from the Four Seasons07:00 Why etiquette matters for founders in 202513:20 Apple x Google: Gemini to power Siri17:24 Apple's AI strategy: Restricting Spend on AI20:04 LLMs vs search the new user behavior shift27:40 Sequoia's leadership handoff36:44 Meme coin corner Jelly's 400M rise and community-led products47:55 Waymo swarms El Camino AI meets the real world50:30 Sam's "No Kings and Queens" merchWe're also on ↓X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessYouTube: https://youtu.be/zv4VdtKpQQkConnect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/brit

BlueRock is building an agentic security fabric to protect organizations deploying AI agents and MCP workflows. With a $25 Million Series A, founder Bob Tinker is tackling what he sees as a 10x larger opportunity than mobile's enterprise disruption. Bob previously scaled MobileIron from zero to $150 million in five years and took it public in 2014. In this episode of Category Visionaries, Bob shares the strategic mistakes that cost MobileIron its category positioning, why go-to-market fit is the missing framework between PMF and scale, and how B2B marketing has fundamentally transformed in just 18 months. Topics Discussed: Taking a company public: the killer marketing event versus the unexpected team psychology challenges of daily stock volatility Why agentic AI workflows create unprecedented security challenges at the action and data layer, not just prompts The strategic timing of category definition: MobileIron's cautionary tale of letting Gartner define you as "MDM" when customers bought for security Where enterprise buyers actually get advice now that Gartner's influence has diminished AEO (Answer Engine Optimization) replacing SEO as the primary discovery mechanism for B2B solutions Why 1.0 categories have fundamentally unclear ICPs versus 2.0/3.0 products with crisp buyer personas The "high urgency, low friction" framework for prioritizing what to build in nascent markets Go-to-market fit: the repeatable growth recipe that unlocks scaling post-PMF Unlearning as competitive advantage for second-time founders GTM Lessons For B2B Founders: Time your category noun definition strategically: MobileIron focused exclusively on solving the problem (the verb) but waited too long to influence category nomenclature. Gartner labeled it "Mobile Device Management" when customer purchase drivers were security-focused, not management. This misalignment constrained positioning for years with no way to correct it. The framework: lead with verb, but proactively shape the noun before external analysts do it for you. Bob's doing this differently at BlueRock by distinguishing "agentic action security" from "prompt security" early, even while the broader market sorts out AI security taxonomy. Use customer language as category discovery, not invention: Bob's breakthrough on BlueRock positioning came from asking prospects: "How would you describe what we do to your peers?" One prospect distinguished their focus on "the action side - taking AI and taking action on data and tools" versus prompt inspection and AI firewalls. This customer-generated framing revealed the natural fault lines in how practitioners think about the problem space. The tactical application: run this exact question with your first 10-15 qualified prospects and pattern-match their language, rather than workshopping category names internally. Engineer for the "high urgency, low friction" intersection: Bob's filtering criteria for BlueRock's roadmap requires both dimensions simultaneously. When a prospect revealed they were building their own MCP security tools - a signal of acute, unmet pain - they also asked BlueRock to add prompt security features. Bob's framework forced a "no" despite clear demand because it would violate low friction. The discipline: if a feature request fails either test (not urgent enough OR too much friction), it doesn't make the cut, even when prospects explicitly ask for it. Accept ICP ambiguity as a feature, not bug, of 1.0 markets: In 2.0/3.0 categories, you can target "VP of Detection & Response" with precision. In 1.0 markets like agentic security, Bob finds buyers across three distinct orgs: agentic development teams building secure-by-default systems, product security teams inside engineering (not under the CISO), and traditional security organizations. His thesis: this lack of crisp ICP definition is actually a reliable signal you're in a genuinely new market. The response: invest in community engagement across all three buyer types rather than forcing premature segmentation. Shift content strategy from SEO to AEO immediately: Bob identifies the clock speed of marketing change as "breathtaking" - what worked 18 months ago is obsolete. The specific shift: ranking above the fold in Google search is now irrelevant. What matters is appearing in the answer box that ChatGPT or Google Gemini surfaces above traditional results. This isn't incremental SEO optimization - it requires fundamentally restructuring content to feed LLM context windows and answer engines rather than keyword-optimizing for traditional search crawlers. Treat go-to-market fit as a distinct inflection point: Bob observed a consistent pattern across MobileIron, Box (Aaron Levie), Citrix (Mark Templeton), Palo Alto Networks (Mark McLaughlin), and SendGrid (Sameer Dholakia) - all hit PMF, hired salespeople aggressively, burned cash, and stalled growth while boards grew frustrated. The missing concept: PMF proves you can create value; GTM fit proves you can capture it repeatedly. It's the "repeatable growth recipe to find and win customers over and over again." The tactical implication: after PMF, resist pressure to scale headcount and instead obsess over making your first 3-5 sales cycles systematically repeatable before hiring your second AE. Build community as primary discovery in fragmented buyer markets: Bob's most different GTM motion versus five years ago: "We're just out talking to prospects and customers - individual reach outs, hitting people up on LinkedIn, posting in discussion boards, engaging with the community." This isn't supplemental to demand gen; it's replaced traditional top-of-funnel. When prospects exist across multiple personas without clear titles, community presence in Reddit, Stack Overflow, and LinkedIn becomes the only scalable discovery mechanism. The benchmark: successful new tech companies have built communities of early users before they've built repeatable sales motions. Practice systematic unlearning as second-time founder discipline: Bob's most personal insight: "What really got in my way wasn't what I needed to learn. It was what I needed to unlearn." The specific application: he's questioning his entire MobileIron marketing playbook because "blindly applying that eight-year-old playbook to marketing or sales will end in tears." His framework: periodic gut checks asking "What assumptions am I making? How should I think about this differently?" rather than letting inertia drive execution. The meta-lesson: success creates muscle memory that becomes liability without deliberate examination. Second-time founders should actively audit which reflexes to preserve versus discard. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Harish spent 9 months building Deliver and could barely get 10 customers. The product worked. Merchants liked the fast delivery promise. But nobody was signing up.Then he made two changes—and scaled to $100M in revenue in 2 years. Shopify acquired them for over $2B.Harish says it wasn't about finding product-market fit. It was about finding product-PRICE-market fit. The product was fine. The pricing model was killing them. This episode breaks down why pricing often isn't just a business decision—it's part of your product, how to build self-serve systems that scale to thousands of customers without talking to anyone, and why you must obsess about end users AND economic buyers if you actually want adoption.Harish is now building Augment, an AI company for logistics that just raised an $85M Series A. He shares what he learned shadow-sitting operators for 60 days and why demos mean nothing in the AI era.Why You Should Listen:Why PMF is often not enough—you need  product-price-market fitWhy subtle changes can have huge resultsWhy you need both users AND buyers to love your productHow to master self-serve Keywords:startup podcast, startup podcast for founders, product market fit, pricing strategy, $2B exit, Shopify acquisition, product-price fit, logistics startup, self-serve systems, Amazon fulfillment00:00:00 Intro00:07:06 Starting Deliver in 201700:14:24 Struggling with only 10 customers after 9 months00:19:53 The two changes that changed everything00:23:43 Zero to $100M in 2 years and product-price-market fit00:29:32 How the $2B+ Shopify acquisition happened00:32:07 Starting Augment AI for logistics00:47:35 PMF moments and top advice Send me a message to let me know what you think!

Dheeraj built Nutanix into a $20B public company—then walked away to start DevRev. He just raised a $100M Series A.This episode breaks down why most founders "sell and run" (chase new logos instead of delivering value), why that strategy fails, and how Dheeraj thinks about building platforms with use cases instead of just features. He explains why the biggest opportunities come from bundling and why you need to hit 130%+ NRR to scale in B2B.Dheeraj also shares the two near-death experiences at Nutanix in the first 5 years, how they survived, and what he's building differently at DevRev in the AI-native world.If you're wondering whether you have real PMF, how to think about platforms vs features, or why your existing customers matter more than new ones—this is mandatory listening from someone who's done it twice at massive scale.Why You Should Listen:Learn why PMF at $1M doesn't mean PMF at $10M—and why you have to find it again at every milestoneWhy "sell and run" kills startups—the real work starts after you close the dealSee how platform thinking (not feature thinking) took Nutanix to $1B ARRUnderstand why 30-40% of revenue from existing customers is real PMF Keywords:startup podcast, startup podcast for founders, product market fit, platform thinking, Nutanix founder, enterprise SaaS, net dollar retention, PMF milestones, fastest to $1B, second-time founder00:00:00 Intro00:01:58 Starting Nutanix00:14:24 Why he left a $20B company00:18:53 The DevRev thesis00:27:39 Pre-AI vs post-AI product strategy and the agent shift00:40:57 Platform vs features00:46:25 PMF is not a destination00:48:10 #1 AdviceSend me a message to let me know what you think!

Ben Alarie spent 8 years building Blue J with "partial product market fit"—real customers, real revenue, but no real market pull. Then he made a bet that would either kill the company or 10x it: he put the existing product in maintenance mode and gave his team 6 months to rebuild everything from scratch using a technology that barely worked.Two years later, Blue J went from $2M to $25M in ARR. They're adding 10 new customers every single day. NPS went from 20 to 84.This isn't a story about getting lucky. It's about a founder who knew—with absolute conviction—that the market would eventually arrive, and made sure he was ready when it did. But it's also about the danger of fooling yourself into thinking you have PMF when you only "kind of have PMF."Why You Should Listen:Learn the brutal difference between fake and real PMFDiscover when to abandon millions in existing ARR to go all-in on something elseWhy "time to value" might be the single most important metric for word-of-mouth.See what it takes to survive until the market is ready.Keywords:startup podcast, startup podcast for founders, product market fit, founder journey, early stage startup, startup pivot, AI startup, SaaS growth, founder advice, hypergrowth startupChapters:(00:02:00) Starting BlueJ(00:9:26) Introducing AI to Tax Research(00:12:44) Starting to Build(00:17:03) Not Having True PMF(00:19:44) Believing in Retrieval Augmented Generation(00:25:34) Updating to V2 of BlueJ(00:30:58) The Necessity of Time to Value(00:33:47) When You Knew You Have PMF(00:38:19) One Piece of AdviceSend me a message to let me know what you think!

It's Philly Music Fest, and we wanted to highlight one more artist playing the fest this year. KulfiGirls are Abi, Joan, Adesole, and Stephanie, and they are all on the show. Nate Runkel of Yo That's My Jawn is sitting in the host chair this week, and he talks with KulfiGirls about it all: how they met, how they formed up as a band, all the work that went into their LP 'Divinity', the pain of mixing your own record (props to Joan on that one), and how they're constantly floored when people come up to tthe band and tell them how much their music means to them. It's a rare feat to get all members of a band on one conversation, and to have it flow so seamlessly. You'll have a love not only for their music, but for how they work together as a group.  KulfiGirls are playing Philly Music Fest on October 18th at Underground Arts with The Wonder Years and Caracara (who were guests way back on #230). Go to the PMF site for more info on that show and all the other shows this week at venues around Philadelphia. KulfiGirls will also be at Milkboy on November 19th, and in Brooklyn at The Meadows on December 13th. The album 'Divinity' is available now wherever you get your digital music. In honor of Philly Music Fest, all sales of our compilation, 'Want To Play A Song?: Live On 25 O'Clock Vol.1', go to strengthen the charitable giving of the festival. If you can't make it to a show and still want to support the work of PMF and their contributions to the music charities of Philadlephia, you can buy a digital copy of the compilation at our Bandcamp.

Neste episódio do Canary Cast, Marcos Toledo, cofundador e General Partner do Canary, conversa com Allan Paladino, cofundador e CEO da Lastro, empresa fundada em 2021 que está reinventando as transações imobiliárias no Brasil com a Laís — sua “primeira funcionária digital” construída sobre o WhatsApp. A Laís já atende mais de 1.000 imobiliárias, engajando clientes 24/7, qualificando leads, marcando visitas, gerando conexões e respondendo dúvidas em tempo real. O resultado é um aumento de até 60% na conversão de leads em visitas, redução do tempo médio de resposta de 6 horas para 15 segundos e uma nova forma de corretores trabalharem com mais eficiência. No episódio, Allan compartilha sua trajetória do mercado financeiro ao empreendedorismo, o pivot estratégico que levou à criação da Laís e como a Lastro está transformando um dos setores mais fragmentados do país em um ecossistema mais inteligente, eficiente e humano.Convidado: Allan PaladinoAllan Paladino é cofundador e CEO da Lastro. Economista formado pela USP, iniciou sua carreira no Credit Suisse, foi CFO e sócio de uma operação de coworking e, em 2021, fundou a Lastro ao lado de José Thomaz. Hoje lidera a expansão da Laís, agente de AI que já conversa com centenas de milhares de brasileiros todos os meses.Host: Marcos ToledoMarcos Toledo é cofundador e General Partner do Canary, um dos principais fundos de venture capital no Brasil. Antes do Canary, Marcos construiu uma sólida carreira no mercado financeiro, iniciando no JP Morgan e depois cofundando a M Square Investimentos, gestora de ativos líder no país.Highlights do episódio:00:00 – 03:00 Boas-vindas e apresentação 03:01 – 08:30 Início da trajetória profissional do Allan08:31 – 13:50 Primeira experiência empreendedora e entrada no mercado imobiliário13:51 – 18:30 Formação da Lastro: encontro com o co-founder, José Thomaz e a tese inicial18:31 – 23:00 Primeiros sinais de PMF, desafios para atingir escala e decisão de pivot23:01 – 28:20 O impacto da chegada da AI generativa e a virada estratégica para criar a Laís28:21 – 33:00 A Laís como “primeira funcionária digital” das imobiliárias: proposta de valor e casos de uso33:01 – 37:00 O impacto da Laís em números 37:01 – 41:00 A diferença entre vender software e “sell work”: a Laís como funcionária que executa tarefas41:01 – 44:30 Implementação e confiança: como convencer imobiliárias a adotarem AI em suas operações44:31 – 48:00 Roadmap de futuro48:01 – 50:30 Aprendizados: erros, acertos e conselhos para empreendedores em busca de product-market fit50:31 – 52:00 Casos curiosos com a LaísGlossário de termos mencionados ao longo do episódio: Pivot — Mudança estratégica relevante no produto/modelo de negócioAI (Artificial Intelligence) — Inteligência artificial; aqui, tecnologia utilizada para automatizar conversas, tarefas e decisões.Generative AI — IA generativa; modelos que produzem texto/áudio/imagem, usados para dialogar e responder clientes.LLM (Large Language Model) — é um tipo de inteligência artificial (IA) projetado para compreender, gerar e interagir com a linguagem humana. Esses modelos são treinados com um volume massivo de textos e dados da internet, o que lhes permite aprender padrões, relações e contextos para executar diversas tarefas linguísticas.Agentic AI — Abordagem em que “agentes” de IA executam tarefas de ponta a ponta (ex.: qualificar lead, agendar visita).ChatGPT — Aplicativo popular baseado em LLM, citado como marco de adoção em massa de IA.OpenAI — Organização por trás de modelos como GPT; o “Playground” foi citado como ambiente de testes.Playground — Interface web que permite a desenvolvedores e usuários testar, experimentar e interagir diretamente com os modelos de linguagem da OpenAI, como o GPT, sem a necessidade de escrever código. Ele funciona como um "laboratório" onde é possível explorar as capacidades e os limites dos modelos, ajustando parâmetros e visualizando os resultados em tempo real.WhatsApp — Canal principal de comunicação/atendimento no Brasil; a Laís opera nele 24/7.Lead — Potencial cliente que demonstra interesse (ex.: pergunta por um imóvel).Lead Qualification — Qualificação do lead; filtrar/priorizar conforme intenção e perfil.Conversion Rate — Taxa de conversão (ex.: de lead para visita ao imóvel).Cold Lead — Lead “frio”, com baixa intenção ou pouca resposta; a IA ajuda a filtrar/evitar gasto de tempo.B2B (Business-to-Business) — Vendas entre empresas; no caso, Lastro → imobiliárias.Onboarding — Processo de implementação/integração inicial da solução no cliente.CRM (Customer Relationship Management) — Sistema de gestão do relacionamento com clientes; integrações recebem resumos das conversas da Laís.API (Application Programming Interface) — Interface para integrações entre sistemas (site, CRM, portais).Product–Market Fit (PMF) — Ajuste produto–mercado; momento em que o produto resolve uma dor crítica e “vende sozinho”.Founder–Market Fit — Aderência entre a experiência/interesse do fundador e o mercado/problema atacado.Selling Work — Em vez de “vender software”, vender a execução de trabalho (a Laís atua como “funcionária digital”).Vertical / Horizontal — Estratégias de foco. Vertical: resolver profundamente um setor (imobiliário). Horizontal: atender vários setores com a mesma solução.Dashboard — Painel com métricas/insights (ex.: bairros mais demandados, canais com melhor retorno).Roadmap — Cronograma de evolução do produtoScale / Scaling — Escalar; crescer com eficiência Pre-IPO / IPO — Financiamento pré-IPO e oferta pública inicial; citados no histórico do convidado.Slack — Ferramenta de comunicação interna; usada para alertas sobre conversas marcantes da Laís.See omnystudio.com/listener for privacy information.

In this episode we talk about team structures. As soon as you have employees, you need to decide how to organize them. What are the best organization structures? What are the trade offs? We are here to help! In this episode we answer questions including:How many people should you have reporting to you?When should you hire leaders like a VP of Sales?Do you need product managers?All of these questions were submitted by listeners just like you. You can submit questions for us to answer on our website TheStartupHelpdesk.com or on X/Twitter @thestartuphd - we'd love to hear from you!Your hosts:Sean Byrnes: General Partner, Near Horizon www.nearhorizon.vcAsh Rust: Managing Partner, Sterling Road www.sterlingroad.comNic Meliones: CEO, Navi www.heynavi.comReminder: this is not legal advice or investment advice.Q1: How many people should you have reporting to you?As a startup CEO, you have to delegate management to avoid becoming a bottleneck. The absolute maximum number of people that should report to you at the early stage is 10. A good rule of thumb is to consider how many one-on-one meetings you can realistically handle every two weeks.Your first hires should be leaders who can build and own major functions of the company, such as product, engineering, or sales/marketing. These leaders provide leverage, freeing you up to focus on other critical areas of the business. Managing too many people or managing people who are not leaders of their own functions can prevent you from executing on your core responsibilities. Remember, you want to hire exceptional people to do important work in areas you can't or don't want to do alone.Q2: When should you hire leaders like a VP of Sales?Hire a leader before your time becomes a bottleneck, but be careful to hire the right person at the right time. A "VP of Sales" is typically a scaler, not a builder. They will likely burn out or fail if your company's sales playbook isn't already baked.Founders should own the sales process until it's proven and repeatable. Bring in a VP of Sales when you have a clear Ideal Customer Profile (ICP), have closed multiple deals beyond the initial founder hustle, and can hand them a clear playbook instead of a puzzle to solve.For a sales team of 5-10 people, a Director of Sales with 3+ years of experience is often sufficient. For a team of 10+, you'll likely want a VP with 5+ years of experience. Find someone who can run the sales team better than you can, allowing you to focus on other aspects of the business.Q3: Do you need product managers?There is a lot of debate on this! Don't hire a product manager too early. The CEO is the de facto Product Manager (PM) until you achieve product-market fit (PMF). The learning loop is too critical to delegate at this stage.You know it's time to hire a PM when:Customers love your product and keep using it.You're seeing organic growth or referrals.Engineers are spending too much time in product discussions instead of building.A PM's role is to be the voice of the customer and ensure engineering time is used effectively. The need for an early PM can also depend on your product. For example, a consumer product may need a PM earlier than a developer-focused tool.

In a high-energy session that sparked nods across the room, Lucille and Marc tackled the shifting paradigms in the SaaS market—and made a compelling case for why vertical SaaS is quickly outpacing horizontal models.Marc opened with a candid assessment of the current SaaS landscape. “What's the flaw in the current market?” he asked. In his view, horizontal SaaS faces serious headwinds:AI is leveling the playing field: Tools like AI-assisted coding have lowered the barrier to entry. Startups can now build and scale to $10–20M in revenue without a CTO, making it easier than ever to launch—but harder to stand out.Enterprise sales are brutal: Horizontal SaaS faces challenges in defining clear ICPs (Ideal Customer Profiles), making it harder to gain traction quickly. This often results in sluggish proof points and delayed product-market fit.Vertical SaaS—companies that serve a single, well-defined industry—has several structural advantages that Lucille and Marc believe make it the smarter play:Clear Go-To-Market MotionWith deep domain knowledge, vertical SaaS teams know exactly how to sell and to whom. Their understanding of customer pain points gives them a clear runway for product adoption.Economic Moats from the StartBy solving a niche problem deeply (rather than broadly), vertical SaaS players build sticky products with defensible positioning. This leads to easier upselling and faster PMF (product-market fit).Composable GrowthOnce established in one vertical, these companies can expand into adjacent markets or layers—embedding financial products like payments, insurance, or lending. That transforms them into mini-operating systems for their customers.AI as an Embedded EdgeAI isn't just a buzzword here—it's embedded into the business model. These companies use AI to build smarter workflows, increase automation, and create differentiated products right out of the gate.M&A and Platform PotentialVertical SaaS allows for cleaner M&A and roll-up strategies, given the homogeneity of the user base. This is significantly harder with broad horizontal plays. Layering in APIs and platforms makes them extensible and scalable.Lucille emphasized that success in vertical SaaS hinges on one key ingredient: deep workflow integration. These companies become indispensable to their customers, reducing churn and increasing lifetime value. It's not about shallow features—it's about becoming mission-critical.“The future is not just SaaS—it's vertical SaaS,” Marc concluded. “That's how you build enduring, category-defining software companies.”

Welcome to The Chopping Block – where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week, we're joined by Gordon Liao, Chief Economist at Circle, to dissect the Stablecoin Wars. From Circle's Arc and Stripe + Paradigm's Tempo, to Solana's native stablecoin push and Hyperliquid's deal, we unpack why everyone suddenly wants their own chain or branded stablecoin. Is this the future of crypto's monetary layer — or just a fragmentation nightmare? We dig into FX use cases, PMF for stablecoins, collective bargaining power of ecosystems, and whether “stablecoin-as-a-service” is the next killer primitive or a liquidity trap. Show highlights

Billy, founder of Intuition Systems (ex–VC/early crypto), digs into the biggest risk in AI: models trained on garbage, regurgitated data and algorithm-shaped behavior. We unpack how attestations, signed data, and web-of-trust reputation can give both humans and AI better intuition at decision time — from picking a chair to routing agentic AI across platforms.Timestamps[00:00] We're being shaped by algorithms; slop-in → slop-out AI [00:02] Billy's path: distributed systems, fintech, game bots, early Bitcoin [00:05] The problem: fragmented info (web + people's heads) & costly context-switching [00:06] What Intuition is: structured expression + rediscovery + rewards [00:08] Attestations & incentives (economic + reputational) without spam [00:12] Can bots game it? Bonding curves, loss for bad signal, web-of-trust filters [00:18] Best content should win: TikTok-style merit + trust primitives [00:19] Use case #1: AI agents—personalization, agent reputation, platform reputation [00:22] Why these reputations must be decentralized (avoid platform capture) [00:23] GTM: unopinionated dev platform + opinionated apps to show PMF [00:24] Biggest challenges: focus, hitting PMF, AI acceleration outpacing products [00:27] Mission: give AI “intuition” via signed, attributed, reputational data [00:28] Reducing AI's recursive slop problem; verifiable attribution with keys [00:30] Humans act differently per platform; algorithms distort expression [00:33] Call to action: build at the AI × Web3 trust layer; join the community [00:35] Layer Zero Ventures origins (people = Layer 0); why Intuition exists [00:37] Events: Korea Blockchain Week & Token 2049; Intuition side eventsConnecthttps://www.intuition.systems/https://www.linkedin.com/company/0xintuition/https://x.com/0xintuitionhttps://www.linkedin.com/in/william-luedtke-b0a3bb5a/https://x.com/0xbillyDisclaimerNothing mentioned in this podcast is investment advice and please do your own research. Finally, it would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.Be a guest on the podcast or contact us - https://www.web3pod.xyz/

Will, Founder of Phonely, shares his raw journey into Y Combinator—including sleeping in his car before a 3AM interview. He reveals how founders should interpret investor rejections, the real path to product-market fit, and top lessons a long the way. A must-listen for anyone navigating the early-stage PMF journey or getting funded! Will Bodewes https://www.linkedin.com/in/william-bodewes/

Transforming breakthrough research into a sustainable company is never simple — especially in hard tech. In this episode recorded in December 2024, Zero Emission Industries CEO/founder Dr. Joseph Pratt and Chief Strategy Officer John Motlow share what it takes to move hydrogen power systems from the lab to the marketplace. We talk about raising money in tough conditions, why government grants can be both a blessing and a constraint, and how to build teams that thrive under pressure. Along the way, they offer candid lessons on funding, hiring, and navigating timelines that rarely go as planned. RUNTIME 51:52   EPISODE BREAKDOWN (2:11) “ I knew the path on how to solve it and knew that there was demand for it, and took the jump out of the national lab to start the company.” (6:36) “ I didn't jump into this with a big network of investors.” (8:57) How ZEI produced the world's first commercial fuel cell ferry. (10:56) Why the company's first hire was a Chief Strategy Officer. (12:53) John Motlow says he wanted to join ZEI “because it was incredibly risky.” (17:06) Crafting ZEI's GTM strategy for the FCV Vanguard, a hydrogen-powered, high-performance speedboat. (21:55) Is ZEI a transportation company, or a clean tech startup? (24:20) When it comes to deep tech, customer requirements are wayfinders for PMF. (29:47) “Government funding and their insights is sort of half the picture.” (35:30) “ To be clear, we talked to a lot of investors who did not agree with our TAM.” (39:09) Why they overindexed on hiring employees who have a background in motorsports. (42:19) Joe's advice for building specialized teams in a competitive market. (47:38) “ Don't slot someone in there and then forget about it: Where are their strengths?” (49:27) What's next for ZEI? LINKS Zero Emission Industries Dr. Joseph Pratt John Moslow FCV Vanguard — Live Demo (YouTube) ZEI Raises $8.75 Million in Series A Funding SUBSCRIBE

How do you pivot from an early idea that didn't work ➝ to building a global SaaS leader trusted by 700+ enterprises?In this full episode of the Prime Podcast, Khadim Batti (Co-founder & CEO, Whatfix) reveals the raw founder journey:The pivot moment that sparked WhatfixHustling with 500 cold emails to close first customersLanding a Fortune 10 client at $25/month (and why logos matter more than price)Why product-market fit keeps evolvingScaling SaaS from India ➝ US with 85+ Fortune 500 clientsRaising $270M and turning investors into growth partnersHow AI is shaping the future of digital adoption

在过去一年，大模型的能力边界被不断推高，文生图，文生视频模型不断完善，一批全新的 AI 原生创业公司正在出现，他们不只是用 AI 做工具，而是在用 AI 重新定义内容、社交、甚至是娱乐的形态。 我们在工具 or 朋友：马斯克入局的 AI 陪伴赛道是真需求还是伪命题？| S9E27 (https://www.xiaoyuzhoufm.com/episode/68930545b85e0f8968a94f5e) 中聊了聊 AI 陪伴这个赛道，那更进一步，行业内对未来的互动娱乐形式有怎样的尝试和想象？对于创业公司，如何做好技术与产品的平衡？对于平台型的企业，又如何与创企合作拓宽 AI 的 C 端应用边界呢？今天我们就请到了 AI 原生创企的创始人和提供 AI 基础设施的平台企业嘉宾，一起来展望 AI 时代全新的互动娱乐方式。 本期人物 张玮，百度智能云副总裁，泛科技业务部总经理 ZhangYi（YI），Feeling AI 创始人兼 CEO 丁教 Diane，「声动活泼」联合创始人、「科技早知道」主播 Yaxian，「科技早知道」主播 主要话题 [01:22] ChinaJoy 新观察：传统游戏正被 AI 解构，用户期待更多「情绪价值」 [06:14] 不只是游戏，Feeling AI 要打造实时互动的 3D 内容共创平台 [15:35] 从 NPC 到全新交互模式，AI 如何重塑游戏行业？ [20:42] 游戏强调「控制」，GenAI 强调「发散」，机会就藏在二者的对撞中 [24:13] 技术生态支持、降低成本、快速试错：平台企业如何赋能创业公司？ [28:08] 成本、实时性与物理仿真催生 2D 与 3D 生成的融合趋势 [32:12] 创业公司如何找到自己的 PMF？ [43:52] AI 互动娱乐的未来：新物种诞生时，总会超出我们当时的想象 点击链接 (https://ourl.cn/Bp3fiX)了解更多百度智能云 AI 创企政策 Untitled https://media24.fireside.fm/file/fireside-uploads-2024/images/4/4931937e-0184-4c61-a658-6b03c254754d/cPCwJA1E.jpeg 幕后制作 监制：Yaxian 后期：迪卡 运营：George 设计：饭团 延伸阅读 NPC (Non-Player Character) 非玩家角色，在游戏中由系统或AI控制，用来推动剧情或与玩家互动。 PMF (Product-Market Fit) 产品与市场匹配度，指产品满足特定市场需求并获得用户认可的状态，是创业公司早期的核心目标之一。 UGC (User Generated Content) 用户生成内容，由用户创作并发布的内容，如视频、文章、图片等。 商业合作 声动活泼商业化小队，点击链接直达声动商务会客厅（https://sourl.cn/9h28kj ） ，也可发送邮件至 business@shengfm.cn 联系我们。 加入声动活泼 声动活泼目前开放开放人才发展伙伴岗、市场部门岗位（节目运营、社群运营、内容营销）和 BD 经理等职位，详情点击招聘入口 (https://eg76rdcl6g.feishu.cn/docx/XO6bd12aGoI4j0xmAMoc4vS7nBh?from=from_copylink) 关于声动活泼 「用声音碰撞世界」，声动活泼致力于为人们提供源源不断的思考养料。 我们还有这些播客：声动早咖啡 (https://www.xiaoyuzhoufm.com/podcast/60de7c003dd577b40d5a40f3)、声东击西 (https://etw.fm/episodes)、吃喝玩乐了不起 (https://www.xiaoyuzhoufm.com/podcast/644b94c494d78eb3f7ae8640)、反潮流俱乐部 (https://www.xiaoyuzhoufm.com/podcast/5e284c37418a84a0462634a4)、泡腾 VC (https://www.xiaoyuzhoufm.com/podcast/5f445cdb9504bbdb77f092e9)、商业WHY酱 (https://www.xiaoyuzhoufm.com/podcast/61315abc73105e8f15080b8a)、跳进兔子洞 (https://therabbithole.fireside.fm/) 、不止金钱 (https://www.xiaoyuzhoufm.com/podcast/65a625966d045a7f5e0b5640) 欢迎在即刻 (https://okjk.co/Qd43ia)、微博等社交媒体上与我们互动，搜索 声动活泼 即可找到我们。 期待你给我们写邮件，邮箱地址是：ting@sheng.fm 声小音 https://files.fireside.fm/file/fireside-uploads/images/4/4931937e-0184-4c61-a658-6b03c254754d/gK0pledC.png 欢迎扫码添加声小音，在节目之外和我们保持联系。 Special Guests: ZhangYi（YI） and 张玮.