Podcasts about ai00

Welcome to The DMF — Discovering Meaning in Film and Acting. I'm Justin Younts, and in this episode I continue my conversation with filmmaker, producer, and author Brent Lindstrom as we explore the intersection of filmmaking, technology, and creative storytelling.We dive into the real challenges filmmakers face during the editing process. Brent shares his experience spending hours editing every second of his short film while dealing with an unreliable computer that constantly crashed. After struggling through that process, he eventually built a powerful editing machine that transformed the way he works and dramatically improved his workflow.Our conversation also explores the growing role of artificial intelligence in filmmaking. While AI tools can streamline certain tasks and assist with production, Brent emphasizes that technology should enhance creativity — not replace it. The craft of storytelling, directing actors, and building meaningful narratives still depends on human insight and artistic vision.In this episode we discuss:• The realities of film editing and post-production• How technology is changing filmmaking workflows• The benefits and risks of AI in film production• Maintaining creativity in an age of automation• Writing and developing complex characters• The importance of feedback when refining storiesBrent also discusses his book “One for the Money, Two for the Soul,” which explores powerful themes through storytelling and examines how creative work can balance artistic purpose with financial realities.Whether you're an actor, filmmaker, writer, or creative professional, this episode offers insight into navigating new technologies while protecting the core principles of storytelling.Join us as we explore the future of filmmaking and how creators can use new tools without losing the heart of their craft.Visit Brent's website:https://lightmindedarts.comCheck out Brent Lindstrom's book One for the Money, Two for the Soul:

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

This episode features Drew Russell, Identity Resilience Platform Owner at Rubrik. Jim McDonald and Jeff Steadman explore the intersection of backup, recovery, and identity security. Drew explains how Rubrik evolved from data backup into a cyber resilience platform with identity as a core pillar. Topics include recovering Active Directory, Okta, and Entra ID after ransomware, Rubrik's "bunker in a box" appliance for immutable air-gapped recovery, proactive posture management, CrowdStrike and Defender integrations, and where AI and non-human identities fit into Rubrik's roadmap. The episode wraps with measuring success for a product you hope to never use, and a detour into watch collecting.This episode was made possible by the support of Rubrik. Learn more at rubrik.com/idacConnect with Drew: https://www.linkedin.com/in/drew-russell-3762411b/Learn more about Rubrik: https://www.rubrik.com/idacConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at idacpodcast.comTIMESTAMPS00:00:00 - Welcome and Introduction00:01:19 - Introducing Drew Russell00:01:36 - How Drew Got Into Identity00:02:43 - What Is Rubrik and What Sets It Apart00:03:38 - From Backup to Cyber Resilience00:05:31 - Where Rubrik Fits in the IAM Landscape00:07:08 - Rubrik's Scale: Clients and Growth00:07:51 - Primary Use Cases: Post-Incident Recovery and AD00:09:09 - Kicking Out Compromised Accounts and ADR00:10:11 - Proactive Threat Detection and Mandiant Integration00:11:28 - Scanning Backups to Find the Clean Recovery Point00:12:14 - The Bunker in a Box Explained00:13:18 - Posture Management and Upstream Tool Integration00:14:19 - AI Agent Swarms and the Future Attack Surface00:15:37 - The Taiwan Bank Case Study: Six Weeks to Rebuild AD00:17:16 - The State of Nevada Incident: $400K and 30 Days00:17:56 - What Recovery Covers: AD, Okta, and Entra ID00:19:26 - Post-Restore Change Management and Whitelisting00:20:08 - How Long Should You Store Backups?00:21:19 - Indexing Identity for Intelligent Recovery Points00:22:29 - Excluding Malicious Actions During Restore00:24:41 - Zero Trust for Rubrik's Own Backups00:26:21 - No Windows, No Virtualization Architecture00:27:49 - Proactive Posture Management00:29:00 - CrowdStrike and Defender Real-Time Integration00:30:48 - Why Tabletop Exercises Often Fall Short00:31:53 - AI Roadmap and Non-Human Identities00:34:22 - The Three Pillars: Data, Identity, and AI00:35:29 - Deployment: SaaS vs. On-Prem00:38:37 - Appliance Sizing and Redundancy00:42:23 - Measuring Success for a Product You Hope to Never Use00:43:46 - The Ludacris Rubrik Commercial00:45:31 - Watch Collecting and the Omega Speedmaster00:53:39 - Drew's Closing WordsKEYWORDSIdentity at the Center, IDAC, Jeff Steadman, Jim McDonald, Rubrik, Drew Russell, identity resilience, cyber resilience, Active Directory recovery, AD backup, Okta recovery, Entra ID recovery, identity backup, ITDR, ISPM, non-human identity, NHI, agentic AI, ransomware recovery, bunker in a box, immutable backup, CrowdStrike integration, Microsoft Defender integration, Mandiant integration, identity disaster recovery, ADR, zero trust, tabletop exercises, posture management, IAM, identity security podcast, cybersecurity podcast

Potrebbe essere iniziato il primo scontro culturale o addirittura una “culture war”, come la chiama la stampa anglosassone, sull'intelligenza artificiale. Dopo l'accordo fra OpenAI e il Pentagono, negli Stati Uniti sono esplose le disinstallazioni di ChatGPT e schizzano i download di Claude: sembra che migliaia di persone stiano scegliendo i chatbot più in base ai propri “valori” che per le loro funzioni. Poi andiamo a Cuba, dove un maxi blackout lascia al buio 7 milioni di persone e riporta al centro la fragilità energetica dell'isola. E infine vi presento la nuova stagione di padre mio.INDICE:00:00:00 - Sommario00:00:57 - Lo scontro etico fra le Ai00:15:48 - Un grande blackout a Cuba00:18:39 - La nuova stagione di Padre MioFonti: https://www.italiachecambia.org/podcast/openai-anthropic-pentagono/Abbonati a Italia che Cambia: https://www.italiachecambia.org/abbonati/ Vuoi sostenere Io Non Mi Rassegno? Abbonati a Italia che Cambia.

Recapping The Dead Files “Tangled” (Season 10, Episode 2) which aired June 22, 2013We kick off our Ohio Three-Fer with shadow snakes, basement rage, and a 19th-century commune that absolutely did not get good Yelp reviews from the town. This house isn't just haunted — it's rooted.Black vine-like tendrils creep through the land and into the living, and we unpack what happens when depression, history, and paranormal energy all tangle together.We talk Free Love backlash, Victorian motherhood (twelve children??), morphine, menopause vs. malevolent spirits, and whether tar water is a reasonable Amazon purchase or a sign it's time to move.It's eerie. It's layered. It's feral.Ponder: If negative energy can embed in land… can it spread like an infection?Witness: Steve calling the commune “woo woo crap” while literally working on a ghost show.Weigh-In: If Amy told you to spray tar water around your entire house — would you stay… or would you move?So, grab your tar water (don't miss a spot!), and join us where… The Activity Continues. Content Warning: We didn't find anything we thought deserved a content warning, but we swear.  Chapter Markers00:00:00 Intro00:05:55 A Word on AI00:08:45 Testing a New Format00:09:41 Side Quest: When You Wash Your Hair00:11:51 Spirit Breakdown00:15:16 Diggin' Tru00:16:38 It Takes a Village00:28:57 Parenting is Hard00:31:10 The Vines00:33:47 The Reveal00:34:33 Clients' Options00:39:34 Steve Getting Dramatic00:40:12 Unstable Ghosts, and Tables00:43:25 Paranormal or Menopause00:49:14 Additional Research Notes00:58:56 Disclaimer/Credits Episode links:Hudson Tuttle: https://en.wikipedia.org/wiki/Hudson_TuttleThe Museum of Talking Boards: https://www.museumoftalkingboards.com/tuttle.htmlCharles Latcha's Suicide Manifesto: https://evermore.imagedjinn.com/blg/9883/suicide-of-a-free-love-at-berlin-heights-july-16-1858/Tar Water on Amazon: https://amzn.to/4qEDcFoOur T-Shirts: https://www.zazzle.com/woodpecker_headache_remedy_t_shirt-256058499501832692Recommend a Dead Files episode for us to recap: https://www.theactivitycontinues.com/recommend-your-favorite-dead-files-epsiode/The Dead Files Official Podcast: https://pod.link/1642377102Amazon links could generate a small commission to us at no cost to you.  The Activity Continues is a paranormal podcast where soul friends Amy and Megan chat about true crime, ghost stories, hauntings, dreams, and other paranormal stuff including the TV show, The Dead Files. Our recaps are full of recurring jokes about recurring tropes.This episode was recorded on February 18, 2026, and released on March 5, 2026. Disclaimer:This podcast is in no way affiliated with Warner Brothers, HBOMax, the Travel Channel, Painless TV, or the TV show The Dead Files or any of its cast or crew. We're just fans who love the show and want to build a community of like-minded people who would enjoy hanging out and discussing the episodes and similar content. Credits:Hosted by: Amy Lotsberg and Megan SimmonsProduction, Artwork, and Editing: Amy Lotsberg at Collected Sounds Media, LLC. https://www.collectedsounds.com/Theme song. “Ghost Story” and segment music by CannelleBackground music: “Beyond the Stars” by Chris Collins Engage!Our website, https://www.theactivitycontinues.com/ Leave us a Voicemail: https://www.theactivitycontinues.com/voicemail/ (might be read on the show)Newsletter sign-up: https://www.theactivitycontinues.com/newsletter          Join us on Patreon: https://www.patreon.com/theactivitycontinuesWe're on (almost) all the socials too @theactivitycontinues SEND US YOUR PARANORMAL STORIES!Email: theactivitycontinues@gmail.com and maybe it will be read on the show!Voicemail: https://www.theactivitycontinues.com/voicemail/ to leave a message and maybe it will be played on the show! BE OUR GUEST!Are you a The Dead Files client, or a paranormal/spiritual professional, and are interested in being interviewed on our show? Let us know by filling out our guest form:https://www.theactivitycontinues.com/guests/intake/ Affiliates/SponsorsPlease see our Store page for all the links for all our current affiliates. https://www.theactivitycontinues.com/store/ Thank you for listening, take care of yourselves. We'll see you next time!If you want to hear us early and ad-free EVERY week, become a Patron, join our Ghosty Fam and get bonus exclusive episodes! https://www.patreon.com/theactivitycontinuesSupport this podcast at — https://redcircle.com/the-activity-continues/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

AIとWebサイト制作の相性がいい理由を解説しました。AIにコーディングを任せて感じたことなど実体験をもとにした話もしています。＝＝＝ 目次 ＝＝＝00:00:00 はじめに00:01:14 AIがプログラミングが得意な理由00:03:08 UIのパターンがほぼ標準化されている00:05:35 HTML・CSSのコードは著作権が認められにくい00:06:51 昔から他人のコードを日常的に使っている00:08:11 ツールの進化の延長線上00:10:05 最近仕事でもコードを書くのはAI00:13:42 これからWeb受託制作への参入は厳しそう00:16:20 エンドトークnote記事「なぜAIとWebサイト制作の相性はいいのか」https://note.com/ashikagacast/n/nc92d99dd5e3e【感想・質問・取り上げてほしいテーマ大歓迎です】✉️メールアドレスashikagacast@icloud.com

Depending on whom you ask, AI is either the best or worst thing that can happen to the next generation. The arguments come from educators, venture capitalists, op-ed writers, and anxious parents—but rarely from the young people in question. On this episode of AI & I, Dan Shipper sat down with one: Alex Mathew, a 17-year-old high-school senior at Alpha High School in Austin, Texas. Alpha School, a rapidly expanding network of kindergarten through grade 12 private schools, is not without controversy. Inside Alpha High School, there are no traditional teachers, all academic content is delivered through an AI-powered platform, and the adults in the classroom, known as “guides,” focus solely on supporting the students emotionally and keeping them motivated to learn. The students have two- to three-hour learning blocks every morning and spend the rest of the day going deep on a project in an area they care about, spanning art, sport, life skills, and entrepreneurship.Mathew's project is a startup called Berry, built around an AI stuffed animal designed to help teenagers with their mental health. His vision is for teens to talk to the plushie for five to 10 minutes a day and, in the process, learn to recognize and cope with their problems in the right way. In this episode, Dan and Mathew talk about what a day at Alpha High looks like, what keeps students from cheating when AI is everywhere, and how Generation Z—people born between 1997–2012—really feels about college, social media, and books. If you found this episode interesting, please like, subscribe, comment, and share! Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper In a world of generic AI, don't sound like everyone else. With Grammarly, you never will. Download Grammarly for free at Grammarly.com.Intent is what comes after your IDE. Try it yourself: augmentcode.com/intentHead to granola.ai/every to get 3 months freeTimestamps: 00:00:00 – Start 00:01:30 – Introduction00:04:08 – A typical day inside Alpha High School00:06:54 – Why Alpha replaced teachers with “guides” focused on motivating students00:12:09 – Why Mathew doesn't use AI to cheat, even though he could00:19:51 – Do ambitious teenagers care about going to college?00:25:12 – Mathew's take on how Gen Z thinks about AI00:27:52 – How Mathew thinks about the effects of social media00:31:29 – Gen Z's relationship with books and reading00:38:57 – Mathew ranks ChatGPT, Claude, Gemini, and Grok00:47:12 – Why Mathew is building Berry, an AI stuffed animal for teen mental healthLinks to resources mentioned in the episode:Alex Mathew: Alex Mathew (@alxmthew)More about Berry: https://berryplush.com/, Berry (@berryaiplushies)

No episódio 106, Arthur, Aíquis e Éfrem atualizam suas experiências com IA, 8 meses depois do outro episódio que fizeram sobre o uso de IA no dia a dia de produto.O que mudou? O que continua igual?Onde tá dando para ganhar mais produtividade no ciclo de produto?Designers finalmente estão adotando AI?Chapters00:06:11 Atualizações sobre o Uso de AI00:11:57 Speech to Text: é mais legal falar que digitar00:20:48 Custom GPTS, Projects vs. Claude Code00:29:46 Prototipando e 'vibecodando' em produção00:37:38 AI no ciclo de Produto. O quanto dá pra ser agentic00:40:03 Paralelização: Mito ou realidade?00:44:21 Comandos, Workflows e casos de uso com o Claude Code00:52:31 OpenClawl: vale o hype?00:57:39 Produtos da SemanaReplit é o nosso novo parceiro oficial!Se você ainda não utilizou o ferramenta de vibe coding número 1 do mundo, resgate 25 doláres em créditos AQUI Bora rodar um hackathon na sua empresa com créditos gratuitos do Replit?Se cadastre em productarena.io/replit e a gente entra em contatoArtigos comentados no programaThere's a short window to get radically ahead by going AI-native. You need to act now - Elena VernaYou've been kicked out of the arena, you just don't know it yet - Claire Vo

Nowe dane o zmieniającym się rynku pracy, Sonyzamyka Bluepoint a EA używa komentatora AI. Zapraszam do materiału! 

WEBINAR LINK:https://shawnmoore.clickfunnels.com/optiniyvvg89sWant to learn more about Vodyssey or start your STR journey. Book a call here:https://meetings.hubspot.com/vodysseystrategysession/booknow?utm_source=vodysseycom&uuid=80fb7859-b8f4-40d1-a31d-15a5caa687b7FOLLOW US:https://www.facebook.com/share/g/16XJMvMbVo/https://www.instagram.com/vodysseyshawnmoorehttps://www.facebook.com/vodysseyshawnmoore/https://www.linkedin.com/company/str-financial-freedomhttps://www.tiktok.com/@vodysseyshawnmooreCONTACT US:support@vodyssey.comSOURCES:1) https://techcrunch.com/2026/02/13/airbnb-plans-to-bake-in-ai-features-for-search-discovery-and-support/2) https://mashable.com/article/airbnb-testing-ai-powered-search-feature3) https://nationaltoday.com/us/ma/boston/news/2026/02/14/airbnb-plans-to-integrate-ai-across-search-discovery-and-support/Chapters:00:00:00 Intro00:02:54 The Impact of AI on Property Management00:05:48 Curating Unique Experiences for Guests00:09:01 The Role of Property Managers in an AI World00:11:50 Strategies for Property Managers00:15:11 Pricing Strategies and AI Integration00:18:06 The Future of Customer Experience with AI00:21:08 Hypothetical Scenarios: Self-Management and Personal Touch

Dr. Jackie Cheung is an Associate Professor at McGill University where he co-directs the Reasoning and Learning Lab. He is also an Associate Scientific Director at Mila-Quebec Artificial Intelligence Institute. He and his team are developing computational models to improve the reliability, pragmatics, and evaluation of large language models to ensure they are contextually appropriate and factually grounded.Jackie was worked as a consultant researcher with Microsoft Research and before his current appointments, he earned his PhD and MSc in Computer Science from the University of Toronto, focusing on computational linguistics, and his BSc from the University of British Columbia.00:00:00 Highlight & Introduction00:02:04 Entrypoint in AI & NLP00:04:47 Academia vs. Industry: Career choices00:09:48 Language Revitalization using AI00:12:24 Addressing Biases & Data sovereignty in language revitalization 00:15:49 Evaluating LLMs as Judges00:17:14 Validity and reliability in LLM evaluation 00:25:11 Evidence-centered benchmark design (ECBD) framework00:30:38 Gaps in LLM benchmarks and meaning of "general purpose" AI00:35:24 General purpose intelligence vs reasoning00:40:16 Safety as an undefined bundle in LLMs00:51:45 Stochastic chameleons: how LLMs generalize and hallucinate 01:03:02 Potential & Biases of agentic frameworks for research01:05:52 Evaluating LLMs for summarization01:11:43 Scaling large language models01:16:33 Advice to beginners entering AI in 202601:20:33 Pitfalls to avoid in AI research & development More about Jackie & his research: https://www.cs.mcgill.ca/~jcheung/About the Host:Jay is a Machine Learning Engineer III at PathAI working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Homepage: https://jaygshah.github.io/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

V 83. epizóde som sa rozprával s Vladimírom Šuchom, dlhoročným odborníkom z európskych inštitúcií, ktorý rieši dopady technológií na spoločnosť a prax okolo AI z prvej línie. V rozhovore sme sa pozreli na to, či nám umelá inteligencia reálne vezme prácu, alebo sa len mení to, čo znamená byť užitočný v novej ekonomike. Prečo je dnes dôležitejšie rozumieť kontextu, než len naháňať nové nástroje?Otvorene sme rozobrali reguláciu. Vladimír vysvetľuje, prečo je predstava „Európa reguluje, preto prehráva“ zjednodušená, a prečo bez pravidiel riskujeme, že AI nebude pomáhať ľuďom, ale začne ich vytláčať alebo vytvárať škody, ktoré už nikto neopraví. Padla aj téma praktickej roviny pre Slovensko. Ak chceme mať v AI obrane a bezpečnosti reálnu rolu, musíme prestať byť pasívni, prestať sa správať ako piesok v motore Únie a začať hrať tímovo, hlasno a užitočne.Silná časť prišla pri sociálnych sieťach. Bavili sme sa o tom, že samotné algoritmy nie sú zlé, ale sú nastavené tak, aby maximalizovali pozornosť. A práve preto prirodzene zistili, že toxický obsah, hnev a konflikt fungujú najlepšie. Výsledkom je spoločnosť, ktorá sa polarizuje rýchlejšie, než ju stihneme opravovať. A keď do toho vstúpia deti, je to ešte citlivejšie. Vladimír hovorí priamo o tom, že deti treba od sietí držať čo najďalej – a namiesto toho im budovať schopnosť sústrediť sa, spolupracovať a komunikovať v realite.V závere sme riešili všeobecnú umelú inteligenciu a otázku, či sa blížime k bodu, v ktorom už nebude AI len nástroj, ale niečo, čo začne výrazne predbiehať človeka. Pre praktickú predstavu sme pomenovali zručnosti, ktoré majú hodnotu bez ohľadu na to, ako rýchlo sa technológie menia. Ide najmä o medziľudskú komunikáciu, spoluprácu a schopnosť premýšľať tak, aby sme vedeli robiť rozhodnutia aj pod tlakom. Tento diel je ideálny pre podnikateľov, manažérov, rodičov a každého, kto chce mať v AI jasno bez paniky a bez bullshitu. Užívajte!---------------------------------------------------------------------------Kapitoly: 00:00:00 – Predstavenie hosťa 00:01:29 – Vezme nám AI prácu?00:07:22 – Musíme regulovať AI?00:12:19 – Európska AI00:14:04 – Slovensko a AI obrana00:16:44 – Všeobecná umelá inteligencia00:23:34 – Algoritmy sociálnych sietí00:25:24 – AI ako nástroj na učenie detí00:30:58 – Aké zručnosti by mal mať každý?00:38:53 – Ľudský vs. technologický vývoj00:43:23– Čo odporúča Vladimír Šucha?00:46:49 – Zmysel života podľa Vladimíra Šuchu---------------------------------------------------------------------------Viac z podcastov nájdete na:https://www.truban.sk/podcast/---------------------------------------------------------------------------Všetky spomenuté knihy a podcasty nájdete v článku na blogu:https://wp.me/p5NJVg-Vl---------------------------------------------------------------------------Podcast si môžete vypočuť aj na streamovacích platformách:● Spotify ▸ https://spoti.fi/31Nywax ● Apple podcast ▸ https://apple.co/3n0SO8F---------------------------------------------------------------------------● Najlepšie z podcastu na Instagrame ●https://www.instagram.com/truban.podcast/● Truban.sk ●https://bit.ly/3r1vYQJ ● Instagram ●https://www.instagram.com/truban/● Facebook ●https://www.facebook.com/miso.truban● LinkedIn ●https://sk.linkedin.com/in/truban

Jutta Leerdam behaalt goud op de 1000 meter in een emotionele finale waarin ze zichzelf en kleindenkend Nederland overtreft. We bespreken de moeite die een groot deel van Nederland heeft met haar succes en de rol van de "zesjes cultuur" die ons al sinds de tweede wereldoorlog klein houdt. Verder nog wat AI, de rol van vraag en aanbod in een economie gedreven door overvloed en een nieuw perspectief op de EU plastic dopjes gateWord bazige baas

A CMO Confidential Interview with Pete Imwalla, former CEO of RPA and 4A's board member. Pete shares his take on how many tech changes resulted in additional agency headcount, how AI is rapidly reversing that trend, and why many agency valuations have dropped significantly over the last 5 years. Key topics include: why brand building is like infrastructure; how Publicis is bucking the trend; how to think about "in-housing;" and why Paul Roetzer's CMO 2023 CMO Confidential show was prescient. Tune in to hear about the "2nd mover advantage" and why he hates the concept of "future proofing." Agency economics are getting rewritten in the age of AI. Mike Linton sits down with Pete Imwalle 32-year RPA veteran and former CEO to dissect what's changing—and what leaders should do about it. They cover the shift from reach to relevance, why FTE-based fees are misaligned in an AI world, how to separate automation from actual advantage, and where in-housing does and doesn't work. Along the way: the sustained business impact of the Farmers “We know a thing or two…” campaign, the rise of agentic workflows, and why “future-proofing” starts with culture, not clairvoyance. Chapters00:00:00 – Cold open + show setup00:00:22 – Mike's intro, Pete's background, and today's topic00:01:18 – Farmers campaign wins Sustained Effie) and effectiveness creativity00:02:18 – 30 years of change: from Prodigy/AOL/CompuServe to Netscape and the open web00:03:24 – Google + broadband: when digital finally changed consumer behavior00:04:33 – Mobile's second wave and the trap of “mobile-first/AI-first” strategies00:06:01 – How agencies adapted: leadership, curiosity, and tolerance for experimentation00:07:42 – Investing ahead of revenue: offense + defense in capability building00:08:22 – Reach fragmentation: from “40% on Cheers” to only the Super Bowl00:09:18 – The real squeeze: boards treating advertising as expense, not investment00:10:13 – Short-termism, PE/VC incentives, and brand vs. performance00:12:21 – “Adapt or die”: AI as an extinction event? (hat tip: Paul Roetzer)00:13:28 – Agentic workflows: shrinking grunt work (esp. media & strategy ops)00:16:00 – Client asks: “give me savings, don't risk my IP”00:16:36 – Why FTE pricing disincentivizes efficiency; pay for outcomes instead00:17:51 – Three futures: AI-native, AI-emergent, or obsolete00:21:39 – Holding-company moves; why Publicis is outpacing peers00:22:00 – Agency valuations: ~40% decline over five years; second-mover advantage in AI00:26:37 – In-housing: when it works, when it backfires, and true cost to own00:28:48 – Build vs. buy: amortization, maintenance, and staying current00:30:16 – The Geico lesson: investing through the curve until returns flatten00:31:22 – What to test by EOY 2026: culture, change management, and low-hanging automation00:34:02 – Ditch “future-proofing”; hire for curiosity and adaptability00:35:35 – Wrap + where to find more CMO ConfidentialTagsCMO Confidential,Mike Linton,Pete Imwalle,RPA,agency economics,advertising,marketing leadership,AI in marketing,agentic workflows,media planning,marketing strategy,brand vs performance,FTE pricing,procurement,in-housing,holding companies,Publicis,Omnicom,Super Bowl ads,Effie Awards,Farmers Insurance campaign,Geico case study,change management,digital transformation,marketing AI,MarTech,measurement,short term vs long term,CMO,CEO,CFO,board governanceSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Andrew Beck, MD, PhD is the Co-founder and CEO of PathAI, where he and his team are developing AI tools to improve the precision of pathology and the efficacy of drug development for diagnosis of cancer and also many other complex diseases.Before founding PathAI, Andrew was an Associate Professor at Harvard Medical School, where his research focused on the application of machine learning to cancer pathology. He earned his MD from Brown University and his PhD in Biomedical Informatics from Stanford University, where he pioneered some of the first computational models used to predict patient outcomes in oncology.Time stamps of the conversation:00:00:00 Highlights00:01:28 Introduction00:02:18 Entrypoint in AI00:07:02 Background in Medicine and Bioinformatics 00:10:00 Leap from academia to entrepreneurship00:16:20 Translating AI developments to Pathology00:21:15 Specialist vs Generalist AI models in medicine00:24:15 What sets PathAI apart?00:26:32 AI adoption medicine00:34:25 Usage of AI tools in clinical workflows, example MASH00:40:10 AI in Dermatopathology00:42:15 AI for biomarker discovery00:47:05 Will AI models replace pathologists?00:52:28 Avoiding over-reliance on AI00:57:40 Is AI living unto the hype?01:01:00 Challenges in clinical trials 01:05:12 AI reaching patients directly01:09:50 Working at intersection of AI & Healthcare01:15:30 Pitfalls to learn fromMore about PathAI: https://www.pathai.com/and Andy: https://www.pathai.com/about-us/andy-beckAbout the Host:Jay is a Machine Learning Engineer III at PathAI working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Homepage: https://jaygshah.github.io/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

In this episode of NeedleXChange I interview Ruth O'Leary.Ruth O'Leary is an award-winning embroiderer who's been exploring AI image generation as a tool for design, iteration, and self-expression.We talk about AI as a “new medium” (and the early chaotic phase of it), parallels with the Jacquard loom and past industrial shifts, the difference between screen-based fakery and physical needlework, and how Ruth uses AI outputs as reference material that gets reimagined through applique + embroidery to speed up making without losing what Ruth loves about stitching.Timestamps:00:00:00 - Introduction00:01:30 - Exploring AI Art and Personal Journey00:04:20 - The Evolution of Artistic Expression through AI00:07:03 - The Unexpected Outcomes of AI in Art00:12:46 - The Impact of Technology on Traditional Crafts00:15:35 - Navigating the Challenges of AI and Authenticity00:18:34 - The Balance of Speed and Craftsmanship in Art00:22:17 - The Role of AI in Realizing Artistic Visions00:25:07 - The Process of Creation and InspirationLinks:Website: rostara.co.ukInstagram: rostara.handmadeIntro music is Far Enough by Shiver Disk via Epidemic Sound.About NeedleXChange:NeedleXChange is a conversation podcast with embroidery and textile artists, exploring their process and practice.Hosted by Jamie "Mr X Stitch" Chalmers, it is an in-depth showcase of the best needlework artists on the planet.Visit the NeedleXChange website: needl.exchangeSign up for the NeedleXChange Newsletter here: bit.ly/NeedleXChangeNewsIf you want embroidery inspiration and regular doses of textile art, visit the Mr X Stitch site here: mrxstitch.comIf you're looking for modern cross stitch designs, then XStitch is the magazine you need!Find out more here: xstitchmag.comAnd follow Mr X Stitch on all the usual social media channels!Facebook: mrxstitchInstagram: mrxstitchPinterest: mrxstitchYouTube: mrxstitchLinkedIn: mrjamiechalmers

Your website might rank #1 on Google but be completely invisible to ChatGPT, Claude, and Perplexity. In this episode, let's break down why a huge chunk of the web is fundamentally broken for AI systems - not because of bad content, but because of technical decisions that made sense for humans but make sites invisible to the AI systems rapidly becoming the front door to the internet.Chapter Timestamps00:00:00 - Introduction: The new game your website is losing00:01:43 - The Scale of the Problem: AI crawler traffic explosion00:05:19 - The JavaScript Problem: Why AI crawlers can't see your content00:10:28 - The Bot Protection Paradox: Accidentally blocking AI00:14:40 - The Speed Requirement: Why 200ms matters00:17:46 - AI Agents Are Struggling Too: Browser agents and their limitations00:20:46 - How to Fix It: 6 things you need to do00:25:33 - Closing: The web is adapting againKey Statistics569 million GPTBot requests on Vercel's network in a single month370 million ClaudeBot requests in the same period305% growth in GPTBot traffic (May 2024 to May 2025)157,000% increase in PerplexityBot requests year-over-year33% of organic search activity now comes from AI agents~40% failure rate for the best AI browser agents on complex tasksThe 6 Things to FixImplement Server-Side Rendering (SSR) - If your site uses a JavaScript framework (React, Vue, Angular) with client-side rendering, switch to SSR or static site generation immediately. Use Next.js, Nuxt, or a pre-rendering service.Add Structured Data with JSON-LD - Expose key information in machine-readable format using schema.org markup. Microsoft confirmed Bing uses this to help Copilot understand content.Optimize for Speed - Target server response time under 200ms. First Contentful Paint under 1 second. Largest Contentful Paint under 2.5 seconds.Check Your Bot Protection Settings - Review Cloudflare, AWS WAF, or your CDN's bot management. Make a deliberate decision about GPTBot, ClaudeBot, and PerplexityBot access.Kill Infinite Scroll and Lazy Loading for Content - Use paginated URLs with standard HTML links. Ensure high-value content is in the initial HTML response.Keep Sitemaps Current - Maintain proper redirects, consistent URL patterns, and fix broken links.Tools MentionedGlimpse - Free tool to test how AI sees your website: glimpse.webperformancetools.comShow LinksSources Referenced in This EpisodeAI Crawler Statistics:Vercel Blog - The Rise of the AI CrawlerCloudflare 2025 Year in ReviewCloudflare - From Googlebot to GPTBotSearch Engine Land - AI Optimization GuideJavaScript Rendering:Prerender.io - Understanding Web CrawlersSearch Engine Journal - Enterprise SEO Trends 2026No Hacks is a podcast about web performance, technical SEO, and the agentic web. Hosted by Slobodan "Sani" Manic.

Jeff Steadman is joined by RSM colleagues Rich Servillas and Charles John to explore the critical intersection of identity access management, operational resilience, and disaster recovery. Rich, a director from the cyber response group, shares insights from the front lines of ransomware and cloud intrusions, while Chuck, director of operational resilience, discusses the importance of business continuity planning. The conversation covers the true impact of security incidents on brand reputation and operations, the necessity of out-of-band communication, and why identity is often the first thing challenged and the last thing trusted during a crisis. The guests also provide practical advice for IAM professionals on reducing blast radius through standing privilege reduction and robust logging.Connect with Rich: https://www.linkedin.com/in/richard-servillas-041a0551/Connect with Chuck: https://www.linkedin.com/in/chuckjohn/Connect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.comTimestamps:00:00:00 - Introduction and 2026 conference outlook00:01:44 - Introducing guests Rich and Chuck from RSM00:03:56 - Defining operational resilience and business continuity00:06:22 - When and how to start the planning process00:09:55 - Chuck's background in public health and emergency management00:12:44 - The broad impact of incidents on brand and operations00:16:45 - Key elements every recovery plan must include00:19:14 - Defining incident severity and matrixes00:21:52 - Identity as the new perimeter and its operational dependencies00:24:57 - Why hackers log in rather than break in00:26:46 - The first hours of a cyber incident response00:29:35 - Current threat trends and the role of AI00:31:29 - Updating plans through post-action debriefs00:34:31 - Cyber insurance gaps and contractual SLAs00:40:24 - Advice for identity professionals on reducing blast radius00:46:10 - Personal milestones and looking forward to 2026Keywords:IDAC, Identity at the Center, Jeff Steadman, Jim McDonald, IAM, Cybersecurity, Business Continuity, Disaster Recovery, Operational Resilience, RSM, Incident Response, Ransomware, Cyber Insurance, Identity Governance

In this episode, Sarah talks about how AI is reshaping the UX landscape and why three key skills are more important than ever for professionals looking to stand out in the evolving job market.Despite the rise of AI tools, it's not about being replaced—it's about evolving your mindset and skills to stay relevant. In this episode, Sarah explains why UX professionals need to stop thinking like "doers" and start thinking like "drivers." You'll also hear why the critical skills of speed, quality thinking, and impact are necessary to stay valuable in your career.This episode is for anyone feeling uncertain about how AI fits into your skillset or wondering how to stay relevant in an AI-powered UX job market.What You'll Learn in This Episode:✔️ Why shifting from being a “doer” to a “driver” is essential for staying valuable in UX✔️ The 3 most important skills for UX professionals in the age of AI: speed, quality thinking, and impact✔️ How to leverage speed without sacrificing quality in your UX work✔️ Why quality thinking (and not relying on AI) is crucial to uncovering nuances and insights✔️ How to make sure your work has true impact—going beyond just delivering information✔️ The danger of de-skilling and how to stay sharp while using AI as a toolTimestamps:00:00 Introduction: The Future of UX Jobs in the Age of AI00:39 Meet Sarah Doody: Your UX Career Guide01:19 The Value of Strategic Thinking Over Task Execution02:05 Three Critical Skills for UX Professionals05:39 Skill 1: Speed with Strategy09:42 Skill 2: Quality Thinking14:52 Skill 3: Impact and Influence19:44 The Risk of De-Skilling in the Age of AI24:50 Conclusion: Be a Driver, Not a Doer25:54 Call to Action: Support the Podcast

Entrepreneur Andrew Wilkinson used to sleep nine hours a night. Now he wakes up at 4 a.m. and goes straight to work—because he can't wait to keep building with Anthropic's latest model, Opus 4.5.Two years ago, Wilkinson was obsessed with vibe coding on AI software development platform Replit. It was thrilling to describe something in plain English and watch an app appear, less thrilling when the apps were always broken in some way, often full of maddening bugs. So he set his app creation ambitions aside until technology caught up with them.Then, a few weeks ago, he started playing with Claude Code and Opus 4.5. It felt, he says, like having a “$100,000-a-month payroll of engineers” working for him around the clock.Wilkinson is the cofounder of Tiny, a company that buys profitable businesses and holds them for the long term. The Tiny portfolio includes the AeroPress coffee maker and Dribbble, a platform where designers can share their work and find jobs. Dan Shipper had him on AI & I to talk about the automations Wilkinson has built for his work and personal life, including an AI relationship counselor, a custom email client, and a system that texts him outfit recommendations each morning. Wilkinson revealed how all of this individual exploration has changed the way he thinks about buying software companies at Tiny.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperReady to build a site that looks hand-coded—without hiring a developer? Launch your site for free at framer.com, and use code DAN to get your first month of Pro on the house!Timestamps:00:00:00 - Start00:01:07 - Introduction00:02:48 - Why Opus 4.5 feels like the iPhone moment for vibe coding00:08:31 - Why designers have a unique advantage with AI00:14:10 - How Wilkinson built a custom email client with Claude Code00:18:13 - An AI trained on your relationship that predicts your fights00:30:40 - Using AI meeting notes to make your life better00:35:11 - Don't inject your opinion into prompts00:40:21 - Wilkinson's Claude Code tips and workflows00:47:59 - Your personal stylist is a prompt away00:53:17 - How AI is changing the way Wilkinson invests in softwareLinks to resources mentioned in the episode:Andrew Wilkinson: Andrew Wilkinson (@awilkinson)The book Wilkinson references in his prompts, when writing copy with AI: Made to StickEvery's compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugi

This week, Steve sits down with Ankit Jain, co-founder and CEO of Infinitus Systems, to talk about why voice-based AI has become one of the most rapidly adopted tools in healthcare operations, what's actually working in the field, and where the hype still outpaces reality. Ankit shares six years of lessons from building AI agents that handle 35-minute medical calls end to end, plus his predictions on what 2026 and 2027 will really look like as enterprises attempt to build their own agents.We cover:Why so much of healthcare still runs on phones, faxes, and portalsHow AI agents are handling long, high-stakes medical calls without going off trackWhat large enterprises now expect around security, governance, and zero-hallucination requirementsWhy providers, payers, and pharma are adopting AI for different operational workflowsWhy 2026 may be the year many health systems try to build their own agents, and why most will return to vendors by 2027—About our guest: Ankit Jain is the co-founder and CEO of Infinitus Systems, the agentic healthcare communications platform that automates high-stakes clinical and administrative conversations at scale. Under his leadership, Infinitus supports 44% of the Fortune 50, and many of the largest healthcare organizations in the US. A serial entrepreneur, advisor, and investor, Ankit has built companies and guided innovation at the intersection of technology and AI. He founded Quettra (acquired by Similarweb), helped launch Google Play and the search engine Cuil, and went on to co-found and manage Gradient Ventures, Google's AI-focused venture fund. His background in building distributed systems and safety-constrained AI, combined with hands-on experience scaling products in regulated environments, gives him a pragmatic perspective on how to design trustworthy AI that earns adoption in healthcare. Ankit frequently works with industry leaders on governance, education, and integration strategies that make automation safe, approachable, and scalable.—Chapters:00:01:38 Introduction to Ankit Jain and Infinitus Systems00:02:54 The journey into healthcare entrepreneurship00:03:55 Inspiration behind Infinitus and its mission00:04:55 Evolution of AI in healthcare communications00:08:12 Navigating competition in the AI healthcare space00:10:30 Defensibility and product development insights00:14:00 AI's role in enhancing healthcare accessibility00:15:20 Go-to-Market strategies and lessons learned00:17:52 Deepening engagement in healthcare workflows00:19:33 Competitive dynamics in healthcare AI00:20:42 Addressing industry concerns and challenges00:22:14 The need for industry self-regulation00:23:40 Navigating consumer privacy and AI interactions00:25:27 The future of jobs in healthcare AI00:28:30 The evolution of healthcare AI00:29:37 Lessons for entrepreneurs in healthcare—Pre-order Halle's new book, Massively Better Healthcare.—

Słuchasz Karolina Sobańska Podcast. W tym programie rozmawiamy o dobrym świadomym życiu, śledzimy trendy i dyskutujemy o tym co dla nas ważne. Gościem odcinka jest Jowita Michalska / https://www.instagram.com/jowita_digital/Bądź na bieżąco :) www.instagram.com/KarolinaSobanska⁠⁠⁠⁠www.karolinasobanska.com⁠⁠ Współpraca: karolina@pasnormal.group00:00:00 intro00:11:48 zaufanie społeczne do AI00:21:44 największe zagrożenia związane z AI00:29:06 jaka czeka nas przyszłość?00:35:06 obiektywność AI00:40:57 regulacje00:46:47 trendy w innych technologiach00:53:06 robotyzacja01:03:51 bądźmy bardziej świadomi

Preston and Seb unpack AI's implications for safety, governance, and economics. They debate AGI risks, corporate centralization, Bitcoin's regulatory role, and Elon Musk's ventures in space and autonomous tech. IN THIS EPISODE YOU'LL LEARN: 00:00:00 - Intro 00:04:37 – Why AI safety and autonomy are increasingly at odds00:11:30 – How AGI could reshape governance and policy-making00:07:40 – Preston's skepticism about AI self-preservation claims00:15:18 – The unintended consequences of AI regulation00:22:15 – How Bitcoin could hold corporations accountable00:20:10 – The dangers of centralizing economic power via AI00:34:45 – Why generalist thinking matters in a post-pandemic world00:37:20 – The role of curiosity and deep reading in future-proofing00:41:59 – How SpaceX is redefining launch economics with reusable rockets00:57:41 – The hidden potential of Tesla's AI chips and compute power Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Clip 1: AI Expert: We Have 2 Years Before Everything Changes! We Need To Start Protesting! with Tristan Harris. Clip 2: Marc Andreessen explains the future belongs to generalists in the AI era. Clip 3: Elon Musk on the Future of SpaceX & Mars. Official Website: Seb Bunney. Seb's book: The Hidden Cost of Money. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to engage in meaningful stock investing discussions with Stig, Clay, Kyle, and the other community members. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bitcoin Fundamentals Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Browse through all our episodes (complete with transcripts) ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance Tool⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Get smarter about valuing businesses in just a few minutes each week through our newsletter, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠sponsors⁠: HardBlock Human Rights Foundation Masterworks Linkedin Talent Solutions Simple Mining Plus500 Netsuite Fundrise References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

In this episode, Mews founder Richard Valtr shares a practical look at how AI is reshaping hotel operations, guest experience, and revenue strategy. The conversation focuses on why hoteliers should design the guest journey first, then apply technology to scale it—along with concrete ideas for monetizing beyond rooms and preparing teams for what's coming next in hospitality tech.Resources:Our previous conversation with Richard: Hospitality is 'eating the world' - here's what it means for your business and your careerBehind the Stays podcast: How He Built Mews — And Rewired the Modern Hotel StaySkift podcast: Rewiring Hotel Tech for HumansModern Hotelier podcast: Helping Hoteliers Monetize the Full 24 ExperienceBook a demo with MewsChapters: 00:02:16 What does Mews do?00:03:28 What is a PMS?00:05:42 The 3 assets in a hotel business00:08:01 Monetization opportunities00:14:19 Opportunities without admin work00:15:44 Experiment: Building customer archetypes with AI00:21:49 "Red teaming" to "destroy" the company00:24:38 Agentic hospitality00:25:55 Future: Mesh of AI-assisted work00:27:13 LLMs as middleware00:29:35 Misconceptions00:31:54 The first thing you need to do00:33:30 AI needs the truth00:35:38 Speeding up training content creation00:36:36 Where to start with using AI00:41:36 Advice for finding technology partners00:42:26 What Richard is proud of at Mews nowThis is a special sponsor bonus episode, sponsored by Mews.A few more resources: If you're new to Hospitality Daily, start here. You can send me a message here with questions, comments, or guest suggestions If you want to get my summary and actionable insights from each episode delivered to your inbox each day, subscribe here for free. Follow Hospitality Daily and join the conversation on YouTube, LinkedIn, and Instagram. If you want to advertise on Hospitality Daily, here are the ways we can work together. If you found this episode interesting or helpful, send it to someone on your team so you can turn the ideas into action and benefit your business and the people you serve! Music for this show is produced by Clay Bassford of Bespoke Sound: Music Identity Design for Hospitality Brands

Tomorrow is the first day of 2026, and to give our listeners a view of the trends that'll shape the year ahead, Dan Shipper had Every COO Brandon Gell on AI & I to discuss their predictions for what's next. They discussed how software will be built, who will build it, and what it will take for truly autonomous AI agents to become a reality.If you found this episode interesting, please like, subscribe, comment, and share! Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps: 00:00:00 — Start00:01:05 — Introduction00:01:34 — Reflections on Every's growth over the past year00:09:38 — What changes when a company grows from 20 to 50 people00:11:55 — How agent-native architecture will change software in 202600:17:13 — Why designers are slated to become power users of AI00:23:24 — The new kind of software engineer who will direct AI agents00:33:42 — Why the next wave of AI training will focus on autonomy

From investing through the modern data stack era (DBT, Fivetran, and the analytics explosion) to now investing at the frontier of AI infrastructure and applications at Amplify Partners, Sarah Catanzaro has spent years at the intersection of data, compute, and intelligence—watching categories emerge, merge, and occasionally disappoint. We caught up with Sarah live at NeurIPS 2025 to dig into the state of AI startups heading into 2026: why $100M+ seed rounds with no near-term roadmap are now the norm (and why that terrifies her), what the DBT-Fivetran merger really signals about the modern data stack (spoiler: it's not dead, just ready for IPO), how frontier labs are using DBT and Fivetran to manage training data and agent analytics at scale, why data catalogs failed as standalone products but might succeed as metadata services for agents, the consumerization of AI and why personalization (memory, continual learning, K-factor) is the 2026 unlock for retention and growth, why she thinks RL environments are a fad and real-world logs beat synthetic clones every time, and her thesis for the most exciting AI startups: companies that marry hard research problems (RAG, rule-following, continual learning) with killer applications that were simply impossible before.We discuss:* The DBT-Fivetran merger: not the death of the modern data stack, but a path to IPO scale (targeting $600M+ combined revenue) and a signal that both companies were already winning their categories* How frontier labs use data infrastructure: DBT and Fivetran for training data curation, agent analytics, and managing increasingly complex interactions—plus the rise of transactional databases (RocksDB) and efficient data loading (Vortex) for GPU-bound workloads* Why data catalogs failed: built for humans when they should have been built for machines, focused on discoverability when the real opportunity was governance, and ultimately subsumed as features inside Snowflake, DBT, and Fivetran* The $100M+ seed phenomenon: raising massive rounds at billion-dollar valuations with no 6-month roadmap, seven-day decision windows, and founders optimizing for signal (”we're a unicorn”) over partnership or dilution discipline* Why world models are overhyped but underspecified: three competing definitions, unclear generalization across use cases (video games ≠ robotics ≠ autonomous driving), and a research problem masquerading as a product category* The 2026 theme: consumerization of AI via personalization—memory management, continual learning, and solving retention/churn by making products learn skills, preferences, and adapt as the world changes (not just storing facts in cursor rules)* Why RL environments are a fad: labs are paying 7–8 figures for synthetic clones when real-world logs, traces, and user activity (à la Cursor) are richer, cheaper, and more generalizable* Sarah's investment thesis: research-driven applications that solve hard technical problems (RAG for Harvey, rule-following for Sierra, continual learning for the next killer app) and unlock experiences that were impossible before* Infrastructure bets: memory, continual learning, stateful inference, and the systems challenges of loading/unloading personalized weights at scale* Why K-factor and growth fundamentals matter again: AI felt magical in 2023–2024, but as the magic fades, retention and virality are back—and most AI founders have never heard of K-factor—Sarah Catanzaro* X: https://x.com/sarahcat21* Amplify Partners: https://amplifypartners.com/Where to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction: Sarah Catanzaro's Journey from Data to AI00:01:02 The DBT-Fivetran Merger: Not the End of the Modern Data Stack00:05:26 Data Catalogs and What Went Wrong00:08:16 Data Infrastructure at AI Labs: Surprising Insights00:10:13 The Crazy Funding Environment of 2024-202500:17:18 World Models: Hype, Confusion, and Market Potential00:18:59 Memory Management and Continual Learning: The Next Frontier00:23:27 Agent Environments: Just a Fad?00:25:48 The Perfect AI Startup: Research Meets Application00:28:02 Closing Thoughts and Where to Find Sarah Get full access to Latent.Space at www.latent.space/subscribe

Learn how to use philosophy to run your business more effectively. Reid Hoffman thinks a masters in philosophy will help you run your business better than an MBA. Reid is a founder, investor, podcaster, and author. But before he did any of these things, he studied philosophy—and it changed the way he thinks. Studying philosophy trains you to think deeply about truth, human nature, and the meaning of life. It helps you see the big picture and reason through complex problems—invaluable skills for founders grappling with existential questions about their business.I usually bring guests onto my podcast to discuss the actionable ways in which people have incorporated ChatGPT into their lives. But this episode is different. I sat down with Reid to tackle a deeper question: How is AI changing what it means to be human? It was honestly one of the most meaningful shows I've recorded yet. We dive into:- How philosophy prepares you to be a better founder- The importance of interdisciplinary thinking- Essentialism v. nominalism in the context of AI- How language models are evolving to be more “essentialist”- The co-evolution of humans and technology Reid also shares actionable uses of ChatGPT for people who want to think more clearly, like:- Input your argument and ask ChatGPT for alternative perspectives- Generate custom explanations of complex ideas- Leverage ChatGPT as an on-demand research assistantThis episode is a must-watch for anyone curious about some of the bigger questions prompted by the rapid development of AI.If you found this episode interesting, please like, subscribe, comment, and share! Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Ready to build a site that looks hand-coded—without hiring a developer? Launch your site for free at framer.com, and use code DAN to get your first month of Pro on the house!Timestamps:00:00:00 - START 00:04:35 - Why philosophy will make you a better founder00:08:22 - The fundamental problem with “trolley problems”00:14:27 - How AI is changing the essentialism v. nominalism debate00:29:33 - Why embeddings align with nominalism00:34:26 - How LLMs are being trained to reason better00:44:52 - How technology changes the way we see ourselves and the world around us00:46:24 - Why most psychology literature is wrong00:52:46 - Why philosophers didn't come up with AI00:56:30 - How to use ChatGPT to be more philosophically inclinedLinks to resources mentioned in the episode:Reid Hoffman: https://twitter.com/reidhoffmanThe podcasts that Reid hosts: Possible (possible.fm) and Masters of Scale (https://mastersofscale.com/)Reid's book: Impromptu https://www.impromptubook.com/The book Reid recommends if you want to be more philosophically inclined: Gödel, Escher, Bach https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567Reid's article in the Atlantic: "Technology Makes Us More Human" https://www.theatlantic.com/ideas/archive/2023/01/chatgpt-ai-technology-techo-humanism-reid-hoffman/672872/The book about why psychology literature is wrong: The WEIRDest People in the World by Joseph Henrich https://www.amazon.com/WEIRDest-People-World-Psychologically-Particularly/dp/0374173222The book about how culture is driving human evolution: The Secrets of Our Success by Joseph Henrich https://press.princeton.edu/books/paperback/9780691178431/the-secret-of-our-success

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Glean started as a Kleiner Perkins incubation and is now a $7B, $200m ARR Enterprise AI leader. Now KP has tapped its own podcaster to lead it's next big swing.From building go-to-market the hard way in startups (and scaling Palo Alto Networks' public cloud business) to joining Kleiner Perkins to help technical founders turn product edge into repeatable revenue, Joubin Mirzadegan has spent the last decade obsessing over one thing: distribution and how ideas actually spread, sell, and compound. That obsession took him from launching the CRO-only podcast Grit (https://www.youtube.com/playlist?list=PLRiWZFltuYPF8A6UGm74K2q29UwU-Kk9k) as a hiring wedge, to working alongside breakout companies like Glean and Windsurf, to now incubating Roadrunner which is an AI-native rethink of CPQ and quoting workflows as pricing models collapse from “seats” into consumption, bundles, renewals, and SKU sprawl.We sat down with Joubin to dig into the real mechanics of making conversations feel human (rolling early, never sending questions, temperature + lighting hacks), what Windsurf got right about “Google-class product and Salesforce-class distribution,” how to hire early sales leaders without getting fooled by shiny logos, why CPQ is quietly breaking the back of modern revenue teams, and his thesis for his new company and KP incubation Roadrunner (https://www.roadrunner.ai/): rebuild the data model from the ground up, co-develop with the hairiest design partners, and eventually use LLMs to recommend deal structures the way the best reps do without the Slack-channel chaos of deal desk.We discuss:* How to make guests instantly comfortable: rolling early, no “are you ready?”, temperature, lighting, and room dynamics* Why Joubin refuses to send questions in advance (and when you might have to anyway)* The origin of the CRO-only podcast: using media as a hiring wedge and relationship engine* The “commit to 100 episodes” mindset: why most shows die before they find their voice* Founder vs exec interviews: why CEOs can speak more freely (and what it unlocks in conversation)* What Glean taught him about enterprise AI: permissions, trust, and overcoming “category is dead” skepticism* Design partners as the real unlock: why early believers matter and how co-development actually works* Windsurf's breakout: what it means to be serious about “Google-class product + Salesforce-class distribution”* Why technical founders struggle with GTM and how KP built a team around sales, customer access, and demand gen* Hiring early sales leaders: anti-patterns (logos), what to screen for (motivation), and why stage-fit is everything* The CPQ problem & Roadrunner's thesis: rebuilding CPQ/quoting from the data model up for modern complexity* How “rules + SKUs + approvals” create a brittle graph and what it takes to model it without tipping over* The two-year window: incumbents rebuilding slowly vs startups out-sprinting with AI-native architecture* Where AI actually helps: quote generation, policy enforcement, approval routing, and deal recommendation loops—Joubin* X: https://x.com/Joubinmir* LinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/Where to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction and the Zuck Interview Experience00:03:26 The Genesis of the Grit Podcast: Hiring CROs Through Content00:13:20 Podcast Philosophy: Creating Authentic Conversations00:15:44 Working with Arvind at Glean: The Enterprise Search Breakthrough00:26:20 Windsurf's Sales Machine: Google-Class Product Meets Salesforce-Class Distribution00:30:28 Hiring Sales Leaders: Anti-Patterns and First Principles00:39:02 The CPQ Problem: Why Salesforce and Legacy Tools Are Breaking00:43:40 Introducing Roadrunner: Solving Enterprise Pricing with AI00:49:19 Building Roadrunner: Team, Design Partners, and Data Model Challenges00:59:35 High Performance Philosophy: Working Out Every Day and Reducing Friction01:06:28 Defining Grit: Passion Plus Perseverance Get full access to Latent.Space at www.latent.space/subscribe

בפרק זה של הפודקאסט "על המשמעות" עו"ד תמיר דורטל מארח את הרב מני אבן ישראל, מנכ"ל מרכז שטיינזלץ ורב בית הכנסת ׳צמח צדק׳ בעיר העתיקה, לשיחה על טראומת השבעה באוקטובר בראי ההיסטוריה וההלכה.הרב מתאר את תחושת הפגיעה האישית והלאומית, וטוען שההלם נובע מאופוריה ישראלית קודמת ומהתחושה ש"פרצו לנו לבית". הוא משווה לאירועים היסטוריים כמו מסעי הצלב, שבהם פגיעות מצומצמות יצרו טראומות אדירות.הדיון עוסק גם בחטופים ובשאלה האם מדינה יהודית יכולה להעדיף את טובת הכלל על פני היחיד. אבן ישראל מתאר את הפער מול תפיסות צבאיות בינלאומיות, אך מדגיש שהמדינה התחייבה לחטופים כבר בתחילת הדרך.מעבר לכך נידונות סוגיות הלכתיות עכשוויות: תקפות המושג "תינוק שנשבה" בעידן לימוד דיגיטלי, אתגרי הטכנולוגיה מהלכות שבת ועד שתלים ביומטריים, והקושי של מערכת הרבנות להתמודד עם קצב ההתפתחויות.לסיום הוא מציע מבט אופטימי על הצורך בחינוך מחדש ועל יכולת היהדות להכיל מגוון דעות.00:00:00 הלם השביעי באוקטובר ושבירת הקונספציה00:04:19 פרספקטיבה היסטורית: ממסעי הצלב ועד היום00:09:12 כישלון המודיעין ודעת הקהל הישראלית00:11:42 זיכרון וטראומה: האם נזכור את האירועים בעתיד?00:18:00 סוגיית החטופים: מוסר יהודי מול ראייה מערבית00:22:11 מנהגי קבורה, קידוש הגוף והשפעת השואה00:30:52 ריבונות מול השפעה אמריקאית במלחמה00:33:38 ועידת הוותיקן ה-2 והשינוי באנטישמיות הנוצרית00:41:41 "תינוק שנשבה" בעידן האינטרנט וה-AI00:51:02 אתגרי ההלכה בעולם של בית חכם וטכנולוגיה01:03:00 האם ניתן לחנך את האסלאם לשלום?01:13:00 מורשת הרב שטיינזלץ: לימוד עצמאי וניהול זמן#פודקאסט #על_המשמעות Support the showתוכנית המנויים "על המשמעות פלוס" ➕: https://bit.ly/MashmaPlus גישה מוקדמת לפרקים

Ready to stop wearing all the hats in your solo business?In 2026, AI integration will be as common as using your smartphone.But if you're a solo business owner who feels behind or overwhelmed by all the new tools, you're not alone.In this episode, I break down how to use AI and why strategy, simplicity, and your authentic voice are the keys to growth in the next wave of online business.What You'll Discover in This EpisodeMy top 3 predictions for 2026 and how they affect solo entrepreneursThe 3 leadership skills that will set you apart in the age of AIWhy content marketing is about to explode, and how to stand out with heart and originality, not just more content.Why your story and perspective are still your biggest business assetsActionable steps for simplifying your offers, building a strong brand voice, and using AI effectively. Key TakeawaysAI isn't a magic fix; it mirrors what you give it. Your clarity and communication matter more than ever.Delegation is now about asking: Should I be doing this, or can AI?Imagination is your competitive edge. Margin gives you the space to innovate.Your brand message and personality are your biggest differentiators in a crowded, AI-generated world.Keep your business focused: one offer, one audience, one problem, one transformation.Join AI Lab for SolopreneursReady to learn how to work smarter with AI and amplify your personal authority?Find out more at https://marisashadrick.com/communityAudio Timestamps:00:00:00 – Intro and why AI content is starting to blur00:01:20 – AI adoption trends and predictions for 202600:03:20 – Strategic use of custom GPTs and tools00:04:50 – Why prompting skills are your new superpower00:05:57 – The explosion of content: blogs, books, and newsletters00:08:02 – 3 leadership skills solopreneurs need for the future00:09:35 – How to simplify your offer, lead magnet, and audience00:12:05 – Rethinking delegation with AI as your assistant00:14:15 – Why imagination is your next best strategy00:16:28 – Protecting your voice and brand in the age of AI00:18:47 – Closing and community invitationSkip Hours of Prompt Trial & Error with ChatGPTWhether you're writing, planning, analyzing, or brainstorming, my C.O.N.T.E.X.T. ™ method transforms ChatGPT into a consistent marketing assistant. No steep learning curve.Free Download!https://marisashadrick.com/prompts If you're ready to grow with effective marketing that actually feels manageable, here's your next move.Inside AI Lab for Solopreneurs, get Custom GPTs, templates, and coaching to grow your business. Visit: https://marisashadrick.com/communityListen to the "Amplify Your Authority" Podcast! Click Here! Rate & Review: If you enjoyed this episode, please take a moment to leave a 5-star rating and review on Apple Podcasts. Tip: Answer these questions inside of ChatGPT (free or paid) and have AI craft your review! How did you discover this podcast? What's your biggest takeaway from this episode? How has this podcast helped your current journey? Thanks so much for taking a few minutes to craft a review!

Traditional software engineering job listings have dropped by 70%, yet Forward Deployed Engineer (FDE) roles have exploded by over 800% this year. We sit down with Mo Fagir, Principal Technical Consultant at ServiceNow, to break down exactly why this shift is happening and how you can pivot your career to ride this AI adoption wave.In this episode, we cover:The massive market shift: Why "pure coding" jobs are declining while FDEs are booming.The exact technical stack and soft skills required to land these high-paying roles.How to overcome imposter syndrome and build a portfolio that gets you hired, even as a junior.Why this isn't just a trend, but the future of how engineering delivers value.Connect with Mo Fagir:https://www.linkedin.com/in/mo-nour-tarigTimestamps:00:00:00 - Intro00:01:14 - Why software jobs dropped 70% while FDEs grew over 800%00:02:55 - Why companies can't implement AI without Forward Deployed Engineers00:05:36 - Is this career path safe for traditional software engineers?00:07:54 - The exact technical stack you need to master today00:10:48 - Moving from engineering scope to product centric thinking00:16:15 - Can juniors and early career devs get hired as FDEs?00:19:12 - How to build a portfolio that gets you hired00:22:17 - Why passion and attitude beat experience in the AI era00:24:33 - How to train yourself to have a sense of urgency00:29:05 - Can introverts succeed in client facing engineering roles?00:32:17 - Lessons learned from interning at NASA and researching AI00:35:09 - Are we in an AI bubble that will burst soon?00:40:34 - Does becoming an FDE risk vendor lock-in for your career?00:43:36 - Final advice for engineers entering the 2025 job market#ForwardDeployedEngineer #FDE #SoftwareCareers

Mostly Growth on YouTube: https://www.youtube.com/@MostlyGrowthMostly Growth on Apple: https://podcasts.apple.com/us/podcast/mostly-growth/id1842238102Mostly Growth on Spotify: https://open.spotify.com/show/3KDtaLaXx1obFp5PUhZ6V3In this episode of Mostly Growth, CJ Gustafson and Kyle Poyar bounce between sharp SaaS insights and delightfully weird internet culture. They start with an unexpected dive into competitive bird-watching apps, then break down recent software mergers like Superhuman + Grammarly + Coda and what they signal about scaling, cross-sell strategy, and private equity mechanics. CJ and Kyle explore why PE portfolios are becoming powerful distribution channels for AI solutions, how niche data “signals” are outpacing generic ones in sales, and share hedge-fund-style tactics for uncovering proprietary business information. The conversation rounds out with “business blunders” — from Y-axis chart crimes to clickbait headlines — and a lively discussion on Spotify's pricing power and how companies should (and absolutely should not) communicate price increases.—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/Slacker Stuff: https://www.slackerstuff.com/Ben on LinkedIn: https://www.linkedin.com/in/slackerstuff/—RELATED EPISODES:The Layer-Cake Playbook for Vertical SaaS Growth | with Roland Ligtenberghttps://youtu.be/yPxWvhPISKo—TIMESTAMPS:00:00:00 Preview and Intro00:01:43 Sponsors – Pulley and Metronome00:04:05 Birding, Big Year & AI Bird Apps00:06:42 Software M&A Trends in SaaS00:08:57 Superhuman–Grammarly–Coda Reverse Merger00:10:34 How Larger Valuations Attract a New Investor Class00:11:55 Streaming Wars & Platform Consolidation00:13:20 Private Equity as a Distribution Channel for AI00:14:48 How PE Firms Drive Multi-Company Expansion00:15:56 AI for Efficiency: The PE CFO Playbook00:17:44 When AI ROI Really Matters in PE00:19:23 Signal-Based Selling: High-Intent Buyer Detection00:21:15 Signal Fatigue & the Hunt for Better Data00:22:50 Why Proprietary Signals Win00:24:05 Creative (and Creepy) Data Tactics00:26:09 Pinterest vs. Google: Aspirational vs. Actual Behavior00:27:17 Blurring Work & Personal Signals in AI Tools00:27:46 The Michael Jordan of Y-Axis Crimes00:30:33 Notion Agents Billboard: “Let Them Cook”00:32:31 Pricing in the Real World: Spotify00:34:60 → 00:35:00 How to Communicate a Price Increase00:35:40 Closing Credits#MostlyGrowthPodcast #PricingStrategy #AIGrowth #GoToMarket #PrivateEquity 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

CoreWeave CFO Nitin Agrawal joins Run the Numbers to unpack the finance engine behind one of the fastest-growing AI infrastructure companies on the planet. CJ and Nitin dive into what it takes to build financial discipline in an environment where business models are being invented in real time, discussing the company's 700% growth last year and massive first-quarter performance as a newly public company. They cover capex strategy, securitizing GPUs, managing billion-dollar revenue backlogs, and structuring incentives for hyperscale deals, all while keeping investors grounded and servers running at full tilt. If you want a front-row seat to finance in the AI arms race, this episode delivers.—SPONSORS:Tipalti automates the entire payables process—from onboarding suppliers to executing global payouts—helping finance teams save time, eliminate costly errors, and scale confidently across 200+ countries and 120 currencies. More than 5,000 businesses already trust Tipalti to manage payments with built-in security and tax compliance. Visit https://www.tipalti.com/runthenumbers to learn more.Aleph automates 90% of manual, error-prone busywork, so you can focus on the strategic work you were hired to do. Minimize busywork and maximize impact with the power of a web app, the flexibility of spreadsheets, and the magic of AI. Get a personalised demo at https://www.getaleph.com/runFidelity Private Shares is the all-in-one equity management platform that keeps your cap table clean, your data room organized, and your equity story clear—so you never risk losing a fundraising round over messy records. Schedule a demo at https://www.fidelityprivateshares.com and mention Mostly Metrics to get 20% off.Sage Intacct is the cloud financial management platform that replaces spreadsheets, eliminates manual work, and keeps your books audit-ready—so you can scale without slowing down. It combines accounting, ERP, and real-time reporting for retail, financial services, logistics, tech, professional services, and more. Sage Intacct delivers fast ROI, with payback in under six months and up to 250% return. Rated #1 in customer satisfaction for eight straight years. Visit Sage Intacct and take control of your growth: https://bit.ly/3Kn4YHtMercury is business banking built for builders, giving founders and finance pros a financial stack that actually works together. From sending wires to tracking balances and approving payments, Mercury makes it simple to scale without friction. Join the 200,000+ entrepreneurs who trust Mercury and apply online in minutes at https://www.mercury.comRightRev automates the revenue recognition process from end to end, gives you real-time insights, and ensures ASC 606 / IFRS 15 compliance—all while closing books faster. For RevRec that auditors actually trust, visit https://www.rightrev.com and schedule a demo.—LINKS:Nitin on LinkedIn: https://www.linkedin.com/in/nitin-agrawal-cloudcfo/Company: https://www.coreweave.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:The Art and Science of a Day-One IPO Pop with OneStream Software CFO Bill Koefoedhttps://youtu.be/kYCn7XNkCBcFrom Facebook's Hypergrowth to Daffy's Disruption: A CFO's Playbook for Saying Yeshttps://youtu.be/bRIZ6oNPGD0—TIMESTAMPS:00:00:00 Preview and Intro00:02:54 Sponsors – Tipalti | Aleph | Fidelity Private Shares00:06:12 Interview Begins: Scaling CoreWeave00:06:52 CoreWeave's Pivot From Crypto to AI00:11:41 Why CoreWeave Is Uniquely Positioned to Lead AI Infrastructure00:13:32 Hiring for Both Scrappiness and Scale00:16:01 Post-IPO Whirlwind: Acquisitions, Debt Raises, and 10-Year Deals00:16:43 Sponsors – Sage Intacct | Mercury | RightRev00:20:13 Managing Investor Expectations With Radical Transparency00:22:39 Doubling Active Power in Six Months00:25:19 Risk-Balanced Capital Deployment: Power First, GPUs Second00:27:12 Financing GPUs With Delayed-Draw Facilities00:29:38 CoreWeave Rated Platinum for GPU Cluster Performance00:32:25 Compute as the Bottleneck for AI Growth00:33:47 Explaining Revenue Backlog Shape & Timing00:35:06 The Strength of Reserved Instance Contracts00:36:07 Giving Tight but Honest Guidance00:40:26 How Mega-Deals Require C-Suite Participation00:42:19 Tackling Revenue Concentration Through Diversification00:44:05 Building an AI-Only Cloud, Not a General-Purpose Cloud00:46:27 Capital Markets Muscle: Raising Billions at Speed00:47:47 Accounting Complexity in a Business With No Precedent00:49:33 Even the CFO Must Unlearn Old Cloud Assumptions00:51:29 Scaling Public-Company Processes in 90-Day Cycles00:54:42 The Couch Fire vs. House Fire Framework00:57:17 Balancing Risk Mitigation With Opportunity Seeking01:00:30 No Downtime for ERP Changes During Hypergrowth01:02:33 Why the Team Stays Energized Despite the Chaos#RunTheNumbersPodcast #CFOInsights #Hypergrowth #AIInfrastructure #FinanceStrategy 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

Send us a textIn this episode, Joe sits down with Vishnu Varma to explore the evolving landscape of cybersecurity and data management. Vishnu shares his journey from India to the US, detailing his experiences at Cisco and the rise of cloud security. They delve into the challenges of managing vast amounts of data in the age of AI, discussing how BonFi AI is innovating in data security. Tune in to learn about the importance of context in data protection and the future of cybersecurity in a rapidly changing digital world.00:00:19 Introduction to Vishnu's Journey00:00:30 Entering the US and Cisco00:02:18 Cloud Security and AI00:02:48 Data Governance and Challenges00:08:47 The Expansiveness of Cloud00:11:00 AI's Appetite for Data00:12:11 Data Security in the JNI Era00:14:29 The Importance of Context00:16:13 Data Used by Enterprises00:22:24 Conclusion and Future Trendshttps://www.bonfy.ai/Bonfy.aiBonfy ACS is a next-gen DLP platform built for the AI era. Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showFollow the Podcast on Social Media! Tesla Referral Code: https://ts.la/joseph675128 YouTube: https://www.youtube.com/@securityunfilteredpodcast Instagram: https://www.instagram.com/secunfpodcast/Twitter: https://twitter.com/SecUnfPodcast Affiliates➡️ OffGrid Faraday Bags: https://offgrid.co/?ref=gabzvajh➡️ OffGrid Coupon Code: JOE➡️ Unplugged Phone: https://unplugged.com/Unplugged's UP Phone - The performance you expect, with the privacy you deserve. Meet the alternative. Use Code UNFILTERED at checkout*See terms and conditions at affiliated webpages. Offers are subject to change. These are affiliated/paid promotions.

SummaryIn this conversation, Troy Cross discusses the transformative experience of parenthood and its impact on personal values and perspectives. He draws parallels between this experience and the skepticism surrounding disruptive technologies like Bitcoin. Cross emphasizes the importance of overcoming fear and negativity associated with new technologies, advocating for a more imaginative and open-minded approach to their potential. He concludes by highlighting the need for a shift in how we perceive technology's role in our lives, urging listeners to think critically and creatively about the future.TakeawaysParenthood transforms values and perspectives significantly.Skepticism towards new technologies is often rooted in fear.Bitcoin represents a shift in how we view money.Critics of technology often fail to imagine its potential benefits.Fear of technology can stem from cognitive biases and social influences.Not all technology will bring happiness or fulfillment.Abundant energy can drastically improve quality of life.We must think like engineers, not just socially.Expectations of technology should be realistic and grounded.Imagination is key to embracing the future of technology.Chapters00:00 Imagining a New Future with Bitcoin and AI00:22 Tennessee as a Hub for Innovation03:05 The Politicization of Bitcoin and Technology's Role in Society10:31 bp soundbite.mp4Keywordsparenthood, disruptive technology, Bitcoin, skepticism, fear of technology, transformative experiences, future of technology, imagination, social bias, energy poverty

How can AI and innovation transform not just the business side of sports, but create truly human-centered fan experiences? In this episode, Christa Stout, the Chief Strategy and Innovation Officer for the Portland Trailblazers, talks with Kate O’Neill about building people-first strategies in professional sports, using AI to impact real human experiences, and lessons in innovation from around the world. Topics covered: The evolving role of Chief Strategy and Innovation Officer in sports Balancing business value with fan, community, and employee impact Approaches to meaningful innovation and international development lessons Building and implementing AI strategy in a sports organization Change management and centering real people in organizational change How generative AI unlocks human potential and personalizes fan engagement Organizational metrics for innovation, inclusion, and impact Upskilling employees and culture change for AI adoption Creating frictionless, joyful fan experiences with technology Connect with Christa StoutPortland Trailblazers WebsiteLinkedIn Episode Chapters00:00:05 – Introduction to the Tech Humanist Show & Guest Overview00:01:33 – The Scope of Strategic and Innovation Leadership in Pro Sports00:02:58 – The Rise of Strategy Roles & Future-Focused Planning00:05:42 – What Makes an Innovative Sports Team?00:07:23 – Lessons in Innovation from International Experience00:09:16 – Change Management: Combining Theory and Impact00:14:54 – Embedding AI Strategy: From Curiosity to Company-Wide Change00:19:15 – Real-World Results: AI's Impact on Employee and Fan Experiences00:22:38 – Humanizing AI: Where Tech Enables Personal Touch00:26:18 – Redefining “Value” in Sports Organizations00:29:38 – Evolving Metrics and Exponential Possibilities with AI00:32:42 – Building Employee Buy-In and Upskilling for AI Adoption00:35:01 – Tools & Anticipated Changes for the Future of Sports Innovation00:37:25 – What True Innovation Could Mean for the Sports Experience00:39:46 – Closing Thoughts, Where to Connect, and Outro

Henrik Werdelin wants to launch a million businesses that each make $1M—and he's doing it with AI.After helping launch Barkbox and Ro Health through his incubator Prehype, Henrik is distilling everything he knows into Audos, a platform that helps you use AI agents to turn your idea into a profitable, lasting company.We had him on AI & I to talk about “portfolio entrepreneurship”—a new breed of entrepreneurship shepherded in by AI, where founders build families of products around the same customer, instead of one moonshot idea. It's a philosophy we hold close to our hearts at Every.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperHead to ai.studio/build to create your first app.Ready to build a site that looks hand-coded—without hiring a developer? Launch your site for free at https://www.framer.com/*, and use code DAN to get your first month of Pro on the house!*Pitch is the AI presentation platform that helps professionals collaborate on, create, and deliver winning slide decks — all while staying on brand: https://pitch.com/use-cases/ai-presentation-maker/?utm_medium=paid-influencer&utm_campaign=every !Timestamps:00:01:33 - Introduction00:02:50 - Dan and Henrik on the new breed of entrepreneurship that AI makes possible00:11:08 - Why Henrik believes the future belongs to a million $1M companies00:16:14 - How to build “relationship capital” with your customers00:21:35 - Why “customer-founder fit” shapes lasting companies00:23:01 - Everything Henrik learned about himself from a decade of building companies00:31:44 - How Henrik finds focus and meaning in the daily chaos00:34:17 - How Henrik is parenting two kids in the age of AI00:50:33 - The way AI can fix what social media broke00:56:59 - What happens when AI agents become part of how we tell storiesLinks to resources mentioned in the episode:Henrik Werdelin: Henrik WerdelinTry Audos: AudosHenrik's new book: Me, My Customer, and AI

Jed Borovik, Product Lead at Google Labs, joins Latent Space to unpack how Google is building the future of AI-powered software development with Jules. From his journey discovering GenAI through Stable Diffusion to leading one of the most ambitious coding agent projects in tech, Borovik shares behind-the-scenes insights into how Google Labs operates at the intersection of DeepMind's model development and product innovation.We explore Jules' approach to autonomous coding agents and why they run on their own infrastructure, how Google simplified their agent scaffolding as models improved, and why embeddings-based RAG is giving way to attention-based search. Borovik reveals how developers are using Jules for hours or even days at a time, the challenges of managing context windows that push 2 million tokens, and why coding agents represent both the most important AI application and the clearest path to AGI.This conversation reveals Google's positioning in the coding agent race, the evolution from internal tools to public products, and what founders, developers, and AI engineers should understand about building for a future where AI becomes the new brush for software engineering.Full Video EpisodeTimestamps00:00:00 Introduction and GitHub Universe Recap00:00:57 New York Tech Scene and East Coast Hackathons00:02:19 From Google Search to AI Coding: Jed's Journey00:04:19 Google Labs Mission and DeepMind Collaboration00:06:41 Jules: Autonomous Coding Agents Explained00:09:39 The Evolution of Agent Scaffolding and Model Quality00:11:30 RAG vs Attention: The Shift in Code Understanding00:13:49 Jules' Journey from Preview to Production00:15:05 AI Engineer Summit: Community Building and Networking00:25:06 Context Management in Long-Running Agents00:29:02 The Future of Software Engineering with AI00:36:26 Beyond Vibe Coding: Spec Development and Verification00:40:20 Multimodal Input and Computer Use for Coding Agents Get full access to Latent.Space at www.latent.space/subscribe

In this episode of Run the Numbers, CJ Gustafson sits down with Sam Jacobs, Founder and CEO of Pavilion, the global community for GTM leaders. Sam shares how getting fired multiple times as a CRO led him to build a business rooted in belonging — one that monetized members first, prioritized intimacy over growth, and turned a Slack group into a multimillion-dollar company. He and CJ unpack the mechanics of community: the tradeoffs between exclusivity and expansion, why venture capital doesn't always fit human-centered businesses, and how Pavilion balances pricing, curation, and access. They also explore the evolution of the GTM function — from the myth of the plug-and-play VP of Sales to how AI is reshaping RevOps, forecasting, and leadership. Finally, Sam reflects on building durable value beyond personal brand and what it really takes to scale trust as a product.—LINKS:Sam Jacobs on LinkedIn: https://www.linkedin.com/in/samfjacobs/Company: https://www.joinpavilion.com/CJ on X (@cjgustafson222): https://x.com/cjgustafson222Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:E120: What does the future of tech look like when it costs $0 to switch software?https://www.youtube.com/live/Cpw2pkq-FXI?si=-0y0tcLTIlIbkmyOCFOs: Want to Outmaneuver Your Competitors? Here's the Jedi Mind Trickhttps://youtu.be/Yte_fe1xF90?si=hVfgdd0Fg0PQuuoSThe Gross Margin Episode with Sarah Wang of a16zhttps://youtu.be/72aP5ohBxvE—TIMESTAMPS:00:00:00 Preview and Intro00:03:05 Sponsors – Mercury, RightRev, and Tipalti00:06:50 Pavilion, Community, and Go-to-Market Leadership00:10:28 Career Tenure and Executive Turnover00:12:55 Compensation Structure and Equity Negotiation00:14:31 Building Wealth Through Equity00:16:30 Sponsors – Aleph, Fidelity Private Shares, and Metronome00:19:36 Managing Wealth, Lifestyle, and Longevity in Leadership00:22:58 Founding Pavilion to Empower Operators00:25:13 Taking Roles for Learning, Titles, and Leverage00:28:47 Contrarian Executives, Team Dynamics, and Leadership Lessons00:30:36 What Makes a Great VP of Sales00:33:23 Revenue, Profitability, and Misaligned Incentives00:35:08 Quota Setting, Forecasting, and Spreadsheet Pitfalls00:39:07 AI in Sales and the Myth of the AI SDR00:40:32 The Future of Playbooks in the Age of AI00:43:38 The Dangers of AI and the Need for Humans in the Loop00:45:27 Monetizing Pavilion – Memberships, Sponsors, and Pricing Strategy00:49:30 Building Higher-Margin Community Businesses00:57:46 Building a Personal Brand with Long-Term Value01:01:52 Closing Credits and Outro—SPONSORS:Mercury is business banking built for builders, giving founders and finance pros a financial stack that actually works together. From sending wires to tracking balances and approving payments, Mercury makes it simple to scale without friction. Join the 200,000+ entrepreneurs who trust Mercury and apply online in minutes at https://www.mercury.comRightRev automates the revenue recognition process from end to end, gives you real-time insights, and ensures ASC 606 / IFRS 15 compliance—all while closing books faster. For RevRec that auditors actually trust, visit https://www.rightrev.com and schedule a demo.Tipalti automates the entire payables process—from onboarding suppliers to executing global payouts—helping finance teams save time, eliminate costly errors, and scale confidently across 200+ countries and 120 currencies. More than 5,000 businesses already trust Tipalti to manage payments with built-in security and tax compliance. Visit https://www.tipalti.com/runthenumbers to learn more.Aleph automates 90% of manual, error-prone busywork, so you can focus on the strategic work you were hired to do. Minimize busywork and maximize impact with the power of a web app, the flexibility of spreadsheets, and the magic of AI. Get a personalised demo at https://www.getaleph.com/runFidelity Private Shares is the all-in-one equity management platform that keeps your cap table clean, your data room organized, and your equity story clear—so you never risk losing a fundraising round over messy records. Schedule a demo at https://www.fidelityprivateshares.com and mention Mostly Metrics to get 20% off.Metronome 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#RunTheNumbersPodcast #Finance #CommunityBuilding #Leadership #GoToMarket 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

My Fintech Newsletter for more interviews and the latest insights:↪︎ https://rexsalisbury.substack.com/In this episode, Anthropic's Nicholas Lin explains how vertical AI agents are reshaping financial services, from building real-time investment models to automating data analysis for some of the world's largest funds. We explore why finance was chosen as Anthropic's first enterprise vertical, the challenges and benchmarks in deploying safe, reliable AI, and how large organizations are integrating these tools across research and operations. Nicholas Lin also shares insights on the next era of AI adoption, collaboration with global partners, and the future role of financial analysts in an agent-powered economy.Nicholas Lin.: https://www.linkedin.com/in/cxl/00:00:00 - Anthropic demos real-time AI financial analyst00:00:39 - Claude's rollout to global enterprise clients00:01:27 - Why verticalize AI models for finance00:02:36 - AI as a solution for complex industry problems00:03:46 - Tackling regulated logic and audit trails00:05:07 - Building “retrieve, analyze, create” agents00:07:11 - Outperforming on industry research benchmarks00:09:13 - Integrating AI with customer feedback loops00:10:14 - Why AI-enabled spreadsheets matter00:13:03 - Partnering with sovereign wealth funds00:15:10 - Data integration and readiness for AI00:17:13 - Changing workflows with live artifacts00:20:04 - Customizing tools for technical teams00:23:19 - Driving product development with design partners00:25:12 - Future of “full-stack” autonomous agents00:27:33 - Solving adoption and change management00:29:01 - Enterprise-wide AI from consulting to accounting00:30:53 - Most bankers still lack AI access00:32:40 - Social impact of automating analyst work00:34:31 - Favorite AI and finance tools00:36:43 - The next wave of AI advances at Anthropic___Rex Salisbury LinkedIn:↪︎ https://www.linkedin.com/in/rexsalisburyTwitter: https://twitter.com/rexsalisburyTikTok: https://www.tiktok.com/@rex.salisburyInstagram: https://www.instagram.com/rexsalisbury/

37signals makes tens of millions in profit every year but Jason Fried isn't all that interested in running a business.Instead, he cares most about making great products—like Basecamp, HEY, and Ruby on Rails—products that are centered around a single, coherent idea. These products are complete wholes, where each piece matters—like a Frank Lloyd Wright house or a vintage car.But how do you create products like that?In this conversation, we talk to Jason about what two decades of building 37signals has been like—and how to build products that have soul.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperListen to Working Smarter wherever you get your podcasts, or visit workingsmarter.aiTimestamps:00:00:00 - Start00:00:32 - Introduction00:02:06 - What architecture, watches, and cars teach us about software00:10:54 - How Jason thinks AI plays into product-building00:20:58 - How developers at 37signals use AI00:25:47 - Jason's biggest realization after 26 years of running 37signals00:29:58 - Where Jason thinks luck shaped his career00:32:41 - What Jason would do if he were graduated into the AI boom00:37:22 - Dan asks for advice on running a non-traditional company like Every00:46:39 - Why staying true to yourself is the only way to build something lasting00:49:38 - Wholeness as the north star for building products—and companiesLinks to resources mentioned in the episode:Jason Fried: Jason Fried (@jasonfried), Jason FriedMore about 37Signals: 37signalsThe book about architecture by Christopher Alexander: The Timeless Way of Building

We have a very special guest this week: Lee Isaac Chung (Minari, Twisters) brings one of his favorite movies of all time, Steven Spielberg's Artificial Intelligence, to the podcast as his Deep Cut Pick! Isaac chats about his awe and love for the film, what he's learnt as a filmmaker from working with Spielberg on Twisters, and his own transition into blockbuster filmmaking. We also discuss Hayley Joel Osment's all-timer of a child performance, the film's divisive ending, Spielberg's masterful blocking, and the film's worldbuilding and depictions of the future. Find the blue fairy at our FREE patreon, discord server, and our socials @ www.deepcutpod.com Timestamps:00:00:00 Introducing Lee Isaac Chung00:01:50 Introduction to Steven Spielberg and our connection with his work00:09:02 Why Isaac chose AI as his Deep Cut00:13:46 First reactions to AI00:18:04 Plot summary and production context00:20:19 Kubrick00:24:48 Minor Barry Lyndon spoiler00:26:24 Spoiler ends00:28:12 Love/hate and Spielberg's touch00:35:38 Hayley Joel Osment's performance 00:41:53 Strategies for directing children00:46:29 Act 2: Jude Law and Flesh Fair00:52:03 Worldbuilding and depiction of AI00:55:46 Ending01:01:13 Spielberg's blocking01:09:24 Isaac's experience moving into blockbuster filmmaking01:13:46 Outro

⚡️ Enterprise Training — upscaile.com

Retrouvez l'épisode en version française ici : https://www.gdiy.fr/podcast/reid-hoffman-vf/“There is no if. There is only when.”That's how Reid Hoffman answers when asked whether AI will revolutionize business, help govern nations, or cure cancer.And he's earned the right to say it. In the six months between his first appearance on GDIY and this one, we've witnessed some of the biggest breakthroughs in AI history.But those same six months have also deepened the rift between the U.S. and Europe.While most of Silicon Valley's leaders are bowing to President Trump's politics of fear, Reid remains one of the few willing to challenge it — defending freedom of speech in its purest form, not the version sanctioned by power.After co-founding PayPal and LinkedIn, he went on to serve on the boards of dozens of companies including OpenAI until 2023, and Microsoft, where he still sits today.He also advises several governments seeking to understand how AI can be deployed responsibly across society — and even in governance itself.In this episode, Reid shares how to unlock AI's full potential, and how he personally uses it every day : to write, to think, and to challenge his own ideas.A brilliant, grounded mind — far from the radical “tech bros” — driven not by hype, but by a genuine belief in progress.TIMELINE:00:00:00 : “AI is a fantastic enablement of human connection”00:10:05 : Reid's best hacks for daily uses of AI00:20:13 : How to help governments deploy AI in society00:31:24 : What Trump's fear-based government is changing in the US00:49:48 : The vibe-coding revolution and why you should pay attention01:01:53 : Curing cancers with AI?01:06:55 : Why everybody should use AI, even retired people01:13:23 : Start a company with $0 in 2025: what would Reid do01:20:14 : Amazon, Microsoft, Google: who's winning the AI race01:28:17 : “Don't be anxious. Be aware and keep pushing.”We referred to previous GDIY episodes : #452 - VO - Reid Hoffman - LinkedIn, Paypal - L'humanité 2.0 : Homo technicus plus qu'Homo sapiens#452 - VF - Reid Hoffman - LinkedIn, Paypal - “We are more Homo technicus than Homo sapiens”#487 - VF - Anton Osika - Lovable - Internet, Business et IA : rien ne sera jamais plus comme avant#487 - VO - Anton Osika - Lovable - Internet, Business, and AI: Nothing Will Ever Be the Same Again#426 - Thomas Clozel - Owkin - Comment casser Big Pharma grâce à l'IA#473 - VO - Brian Chesky - Airbnb - « We're just getting started »#473 - VF - Brian Chesky - Airbnb - « Après 17 ans, nous ne sommes qu'au début de notre histoire »A few recent episodes in English : #437 - James Dyson - Dyson - “Failure is more exciting than success”#431 - Sean Rad - Tinder - How the swipe fever took over the world#475 - VO - Shane Parrish - Farnam Street - Clear Thinking: The Decision-Making ExpertWe spoke about :Possible Podcast - Reid HoffmanEleven Labs7 magic products of GoogleManas AIThe Lafayette FellowshipSquarespaceOpenAI Jobs PlatformReading Recommendations :Superagency - Reid HoffmanThe Emperor of All Maladies - Siddhartha MukherjeeA Brief History of Intelligence - Max BennettThe Road Less Traveled - Scott PeckCheck out Reid's YouTube channel and his podcast Possible.You can also follow Reid on LinkedIn (of course) and on Instagram.Interested in sponsoring Generation Do It Yourself or proposing a partnership ? Contact my label Orso Media through this form.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

What can AI optimize—and what can only a person transmit?This episode invites a curious look at where technology ends and yoga begins. A seasoned teacher explores the uneasy mix of excitement and fear around AI: the urge to use smarter tools vs. the risk of losing something essential. Instead of answers, you'll get questions that linger:If a sequence is flawless, why can it still feel empty?How do you tell support from substitution?When does help from AI become dependent?You'll hear a thought experiment about a “perfect” robot-led class, a challenge to our habit of outsourcing judgment, and a reminder about what lands only when two people share a room: presence, accountability, being truly seen. If you're curious about using AI without losing your craft—or your why—press play and decide for yourself.—-------—-------—-Episode Chapters:00:00:00 Introduction00:01:10 AI in the Body: Fear or Freedom?00:02:28 Byron's First Feeling: Fear + Excitement00:04:53 Will AI Replace Yoga Teachers?00:05:58 AI as Tool, Not Replacement00:08:31 The Risk of Blindly Trusting AI00:10:59 Thought Experiment: Robot-Led Yoga00:11:19 What Only Humans Bring: Presence & Touch00:15:07 Don't Outsource Your Prefrontal Cortex00:17:31 Perspective: We're Early in AI00:18:57 Tipping Points & Cultural Adoption00:21:04 Mastery and Being Irreplaceable00:22:49 Human Connection > Perfect Cues00:24:19 Technology Changes; Values Remain00:27:27 Intentions at the Center00:28:14 Closing—-------—-------—-

Artificial intelligence is one of the most powerful forces reshaping the global economy, technology, and investing. But understanding AI requires looking at the full story — where it began, how it is unfolding, and where it is headed next.With recent headlines in OpenAI from its $100bn Nvidia investment to its release of Sora, this special in-depth episode of The Bid brings together highlights from across our conversations with BlackRock experts to trace the arc of AI's evolution: its origins, today's massive infrastructure build-out, the unprecedented power demand it creates, its adoption across industries, its geopolitical stakes, and what lies ahead for investors.Key themes:The history and milestones that shaped AI as an investment themeThe massive infrastructure and capital fueling the AI build-outWhy AI's energy demands could reshape global power consumptionHow AI adoption is boosting productivity and changing workAI as a geopolitical competition between nationsWhat the exponential future of AI may bring for marketsKey moments in this episode:00:00 Introduction to a reflection on AI00:20 The Evolution of AI: From Theory to Practice00:56 The Investment Landscape of AI01:45 Historical Milestones in AI05:08 The Build-Out Phase of AI Infrastructure07:25 Energy Demands of AI09:43 Adoption and Transformation of AI10:57 AI in Geopolitical Competition12:09 The Future of AI: Layers of Opportunity16:16 Conclusion and Investor InsightsCheck out this playlist to learn more about AI investing: https://open.spotify.com/playlist/1rt6kLl0fzg9D7puEkAupq

00:00:00 Intro - Oh God, the AI00:03:30 Briar's Garage Golf Sim00:06:00 Fran's Arm Nerve Issue00:11:25 Watts Is a Real American 00:20:40 Yotei Impressions Round 200:35:10 Battlefield 6 00:47:42 Arc Raiders Resets01:13:00 60-percent of US game players only buy two games or fewer01:26:50 Valor MortisJoin our DCP Discord Server!https://discord.gg/dcp--------------------------------------------------------We have a new merch store! Exclusive t-shirts and more incoming!https://store.streamelements.com/dcp_liveSave 5% on Scuf Gaming with code "DCP"https://scufgaming.com/----------------------------------------------------------------------------------------------------------------Find all of the DCP Members on Twitter: @teft | @TheBriarRabbit | @myelingames | @Mrs5oooWattsaArt by Ash: @AR_McDSocial Media and Twitch Management by Mr_Ar3s: @Mr_Ar3s

Today's guest is quite literally one of the greatest songwriters of all time.He also happens to be one of the biggest artists of all time. Imagine that.This legend needs no introduction...And the writer is... Ed Sheeran!Presented by our friends at NMPA.A huge thank you to our sponsors.NMPA fights for songwriters' rights, ensuring fair pay and protection.Check them out at NMPA.org and follow them on social media at ⁨@NMPAorg⁩Splice is the industry's top sample library—royalty-free for all. It empowers creators with its incredible library and ethical AI tools, supporting original artists every step of the way.Download Splice today and follow them on social media at ⁨@splice⁩Chapters:00:00:00 - Intro00:02:05 - Ed and Ross Reconnect00:04:41 - From Rap Battles to Shaping His Songwriting00:07:07 - Painting as Creative Outlet00:09:13 - Emotional Ties to Songs, Writing Honest Songs for Himself and Others00:12:05 - Childhood Music: Beatles, Dylan, and Irish Trad Influences00:15:07 - Fatherhood's Impact: Balancing Music and Family00:16:26 - Ed Sings His First Song!00:18:30 - Ed Sings, Ross Tests His Song Memory00:20:27 - The Key to Confidence Starts With....00:22:10 - Advice: Navigating the Music Industry's Challenges00:24:20 - Where Ed Gets His 'Buzz' From Music00:25:50 - The Thing That Made His Songwriting 'Stick'00:28:35 - No Plan B: Why He Never Gave Up00:30:04 - Discovery by Jamie Foxx: Breaking into the Scene00:36:14 - Mid-Roll Ad: NMPA Fighting for Songwriters00:36:52 - Mid-Roll Ad: Splice Supporting Creators with AI00:44:11 - First Cuts: Writing for Olly Murs and One Direction00:46:58 - Creative Process00:52:12 - The Lawsuit That Changed Everything00:55:49 - Protecting Songwriters: Why No Union Exists01:05:57 - Making "Perfect" to Prove Himself After the Lawsuit01:12:49 - Advice to Young Songwriters...01:14:03 - Legacy Songs: What Ed Wants His Grandkids to Hear01:15:23 - Family Over Fame: Being a Present Dad01:20:38 - Songs Ed's Jealous Of01:23:46 - Closing: Ed's Gratitude and Future PlansAnd The Writer Is...Hosted by Ross GolanExecutive produced by Joe London and Jad SaadFollow us on socials at @andthewriteris ​ Hosted on Acast. See acast.com/privacy for more information.

In this weeks episode, the Mayor and Toby discuss the intersection of technology and insurance, focusing on the challenges and opportunities within the industry. They explore the role of AI in transforming insurance processes, the importance of understanding client needs, and the potential for technology to attract new talent to the field. Toby shares insights from his journey in the insurance and tech sectors, highlighting the innovative solutions his company, OneFort, is developing to streamline operations and enhance client relationships.TakeawaysAI is transforming the insurance industry by automating processes.Understanding client needs is crucial for effective insurance solutions.Technology can attract new talent to the insurance field.OneFort focuses on independent insurance agents to maximize impact.Manual processes in insurance are time-consuming and need automation.Cross-selling and upselling are key strategies for insurance growth.AI can help identify underinsured clients and recommend solutions.The insurance industry faces a talent shortage that technology can address.OneFort integrates with existing tools to streamline insurance workflows.Toby emphasizes the importance of continuous innovation in tech solutions.Chapters00:00:02 Introduction and Greetings00:00:05 Discussing Upcoming Events00:00:20 The Role of Magic in Trade Shows00:03:23 Toby's Journey in Insurance and Tech00:07:55 Miscommunication in Insurance and Tech00:16:21  Focus on AI for producers, and CSR's 00:29:01  AI Solutions for Insurance00:32:04 Cross-Selling and Upselling with AI00:39:32 Future Plans and Industry GrowthSponsors:Smart Choice The Fastest growing agency network in country! Hands DownCanopy Connect - Your 1 click solution to getting the dec pages you need to quote your prospects