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Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I've ever seen ship on a frontier model. It's state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we've seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There's quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let's dive in! ThursdAI - Let me catch you up, every week!

Alla pratar autonoma agenter. Få pratar om vem som håller i tyglarna när de släpps in på företaget. Det är där de flesta piloter dör.Asif Mithawala leder Cloud AI Platforms på Microsoft, ett team på 25 solution engineers, och kommer närmast från AWS. Han dammsög hela Microsoft Build 2026 på en helg, över 400 sessioner. Tldr; året då AI går från något du pratar med till något som jobbar åt dig.Agent 365 var hans personliga favorit på eventet och i avsnittet får du höra varför. Hur Microsoft Scout faktiskt fungerar i Teams och Outlook. Varför Frontier Tuning kan ge en modell som är runt 10 gånger billigare än de största Frontier Labs-modellerna utan att tappa kvalitet på er uppgift. Asif förklarar också nya GitHub Copilot-appen som startar parallella agenter per bugg via git worktrees. Och vad de öppna WorkIQ-API:erna betyder för en partner som vill bygga ovanpå Microsoft 365.Lyssna om du funderar på hur ni går från rolig demo till något ni vågar köra skarpt på måndag morgon.Kapitel:00:00 Intro och Asifs bakgrund03:00 Vad en solution engineer faktiskt gör06:00 Build 2026 sammanfattat på en mening08:00 Microsoft Scout, den första autopiloten13:30 Loopen som gör en agent till en agent18:00 Agent 365, kontrollplanet för digitala medarbetare24:00 Foundry och IQ-lagret, WorkIQ-API för partners30:00 MAI-modellerna och Frontier Tuning42:00 Nya GitHub Copilot-appen och git worktrees45:00 Tre saker att börja med redan på måndagLänkar:Asif Mithawala (LinkedIn)Johan Wallquist (LinkedIn)Securing code, agents, and models across the development lifecycleLaunching seven new MAI modelsGitHub Copilot app: The agent-native desktop experienceBuild 2026 (nyhetssajt) Hosted on Acast. See acast.com/privacy for more information.

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The TLDR here is simple: 'BEEF' Is the Best Show on Netflix right now.But hop in anyway because Ebube's summoned AJ and Soonen for a union that's decidedly NOT fueled by road rage or deteriorating romance. The trio get into everything from who's actually in the right this season, to what the show has to say about the systems that feed the human conditionThey talk about how this season is as much about class warfare as individual conflicts (4:27), in line with what this season of the show is truly about (6:31); as well as the richness of the performances (9:49), Josh and Lindsay's delusion (19:49), how power changes people (23:35) and why it's important for young men to have solid role models (30:02).Art does mirror life after all.You can support us here.Also available on YouTube.Host: Ebube UbochiGuests: AJ & SoonenProduction by: Ebube Ubochi

Everybody has books, emails, and even text threads they intended to read but never got around to because it just felt like too much. So, you skimmed it. You got a sense of it. You can fake your way through a conversation about it. But it feels like so much work to dive in and dissect it. And that might be ok with a high school reading assignment, a text thread with golf buddies, or an all-staff email from HR. But what about a message from God, explaining the purpose of life and how to get the most out of it. Isn't that what the Bible is? Maybe. But where do you start and how do you make sense of it? Is it even possible for an average person to get something significant out of this ancient book on a daily basis?

Warum erscheint Natur in modernen Gesellschaften häufig als etwas, das dem Menschen gegenübersteht? In „Kapitalismus im Lebensnetz“ (2015) entwickelt Jason W. Moore eine grundlegende Kritik an der herkömmlichen Trennung zwischen Natur und Mensch/Gesellschaft. Dieser Dualismus, so Moore, verleitet uns dazu, Natur als etwas Externes zu begreifen: als passive Materie und Ressource, die es für ökonomische Zwecke zu klassifizieren, zu verwerten und – wenn es sein muss – auch zu zerstören gilt. Doch der Mensch und die Gesellschaft sind der Natur nicht äußerlich, sondern immanenter Teil von ihr. Vor diesem Hintergrund muss auch die Geschichte des Kapitalismus als untrennbar von der Geschichte der Natur begriffen und analysiert werden. Der Kapitalismus, so seine These, operiert schließlich nicht jenseits von Natur, sondern ist selbst eine Weise, (menschliche und nichtmenschliche) Natur zu organisieren. Die Entstehung des Kapitalismus verortet Moore daher nicht primär in Fabriken oder Märkten, sondern in historischen Prozessen der Erschließung sogenannter „billiger Natur“: Arbeitskraft (und ihrer Reproduktion), Nahrung, Energie und Rohstoffe. Die Logik kapitalistischer Akkumulation habe zu den ökologischen Umwälzungen geführt, die heute in der Klimakatastrophe sichtbar werden. Wer trägt also die Verantwortung für die ökologische Katastrophe: die Menschheit als Ganze oder eine spezifische Form gesellschaftlicher Organisation? Zu Gast bei Alex Demirović ist in dieser Folge der Forstwissenschaftler Oliver Pye. Kontakt, Kritik, Feedback: theoriepodcast@rosalux.org

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We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

People say reading the Bible changed their life. But how? You've been doing it for a while, and it hasn't changed much of anything. You downloaded the app. You subscribed to the verse of the day. You bought a study Bible. You've even highlighted a couple things. And all that is great. But you still struggle with anxiety. You're still addicted to your phone. You still yell at your kids from time to time. You still wonder if you've wasted your life every year on your birthday. I thought Scripture was supposed to change you, turn you into a new person, and make your life better. Why isn't it working for you? Is the whole thing a scam? Are you just doing it wrong? What gives? If you're new with us, let us know how we can be praying for you, we invite you to fill out an online Connect Card by visiting https://southhillschurch.churchcenter.com/people/forms/91550—If you are looking for what is next for you, we invite you to fill out an online “Next Steps” card by visiting https://southhillschurch.churchcenter.com/people/forms/672517To give with us select the Give tab on the Church Center App or visit https://southhills.org/giving/ and select the Corona Fund or Corona BOW Fund—Visit our Linktree to find out more about everything mentioned in today's message or follow along with the message slides:https://linktr.ee/SouthHillsCorona —To RSVP for On-Campus Events select the Events tab on the Church Center App or visit https://southhills.org/corona/ TL:DR - Guest Speaker: Eric West “Why Isn't It Working?” 5.31.26 -

Everybody has books, emails, and even text threads they intended to read but never got around to because it just felt like too much. So, you skimmed it. You got a sense of it. You can fake your way through a conversation about it. But it feels like so much work to dive in and dissect it. And that might be ok with a high school reading assignment, a text thread with golf buddies, or an all-staff email from HR. But what about a message from God, explaining the purpose of life and how to get the most out of it. Isn't that what the Bible is? Maybe. But where do you start and how do you make sense of it? Is it even possible for an average person to get something significant out of this ancient book on a daily basis?

All the fun and insight of GameFace in 1/3 of the time with improved production values! Reviews of LEGO Batman: Legacy of the Dark Knight and Yoshi and the Mysterious Book! Plus, Not E3/Summer Game Fest 2026 previews for PlayStation, Xbox, Nintendo, and third-party!

Hilary Dubin is co-CEO and head of Jones' digital product & behavioral support program. She graduated from the University of Pennsylvania magna cum laude, majoring in cognitive science with a concentration in computation and cognition, an honors thesis on the effects of gender, realism, and role of virtual agents, and a minor from Wharton in consumer psychology.  She worked in David Brainard's visual neuroscience lab for 3 years and published 4 papers and supplementary materials on illumination discrimination (color perception). After Penn, she was selected as one of ten Americans to be a Ventures Fellow in the Excel Ventures incubator program in Tel Aviv, and continued on to be the inaugural member, and later program lead, of the US Associate Product Manager Program at Atlassian.  She worked as a product manager at Atlassian for 5 years, ultimately as Head of Confluence Editions & Admin Experience where she launched Confluence Premium & Free into multi-million dollar product offerings with 2M+ users. She hired & managed two PMs and lead a team of over 30 developers.  Prior to founding Jones, she and Caroline founded Cozier together, a sleep & loungewear brand designing ethical, effortlessly chic garments for every/body. Hilary started vaping casually in 2017 when the JUUL seemed relatively harmless and fun.  When the world went on lockdown in 2020, her casual vaping habit became a daily crutch for coping with stress and working from home. After over a year of unsuccessful cold-turkey quit attempts, she finally kicked her vaping habit in 2022 when Caroline suggested she try NRT.  Outside of work, Hilary loves hiking, backcountry skiing, trying to find the best burger in NYC, and playing with other people's dogs. In This Conversation We Discuss: [00:00] Intro [02:34] Creating products from personal pain points [06:52] Sponsor: Klaviyo  [08:59] Meeting potential customers where they are [10:47] Adapting products based on user feedback [13:48] Testing market demand with waitlists [16:02] Sponsor: Electric Eye [17:10] Maximizing personal networks for growth [18:34] Gathering behavioral data in early days [19:52] Callouts [20:02] Launching a product to engaged audiences  [22:09] Sponsor: Intelligems [24:09] Pivoting marketing to bridge early limitations [26:24] Driving organic traffic with relatable content  [30:33] Adding modern value to traditional products Resources: Subscribe to Honest Ecommerce on Youtube Nicotime mints and social app to quit vaping quitwithjones.com/ Follow Hilary Dubin linkedin.com/in/hilary-dubin-374156b4/ Follow Caroline Vasquez Huber linkedin.com/in/caroline-vasquez-huber Book a demo today at intelligems.io/ Schedule an intro call with one of our experts electriceye.io/connect Get your free demo klaviyo.com/honest If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

Nobody accomplishes anything truly impressive overnight. They do it over time. I've never heard of a doctor who finished med school in one sitting. Or a gold medal gymnast who picked up the pommel horse that morning. No. Each of these people broke their big goal into smaller steps, and inched their way forward, little by little. It wasn't accidental. It was intentional. And if you want Scripture to change to become the basis for how you think, talk, and live, it's going to happen in much the same way. There's a path to becoming a doctor. There's a path to becoming an Olympian. What's the path to becoming a Jesus follower reliant on Scripture? And is it realistic for someone like you?If you're new with us, let us know how we can be praying for you, we invite you to fill out an online Connect Card by visiting https://southhillschurch.churchcenter.com/people/forms/91550—If you are looking for what is next for you, we invite you to fill out an online “Next Steps” card by visiting https://southhillschurch.churchcenter.com/people/forms/672517To give with us select the Give tab on the Church Center App or visit https://southhills.org/giving/ and select the Corona Fund or Corona BOW Fund—Visit our Linktree to find out more about everything mentioned in today's message or follow along with the message slides:https://linktr.ee/SouthHillsCorona —To RSVP for On-Campus Events select the Events tab on the Church Center App or visit https://southhills.org/corona/ TL:DR - Adam Smith “From Roulette To Routine” 5.24.26 -

Sind Sie schon einmal über die Abkürzung TLDR gestolpert und wussten nichts damit anzufangen? Und was genau ist eigentlich ein Spoonerism? May McCreary aus den USA und Lorraine Turner Akcakaya aus Großbritannien helfen Ihnen diesbezüglich weiter. Die beiden Spotlight-Redakteurinnen sprechen außerdem mit Holger Stark, stellvertretender Chefredakteur der ZEIT, über sein neues Buch Das erwachsene Land. Sind Deutschland und Amerika überhaupt noch Freunde? Ist Donald Trump nur ein Zwischenspiel in der Beziehung beider Länder, oder geht mit ihm eine Epoche der Kooperation unwiderruflich zu Ende? Was bedeutet es für Deutschland, für Europa und für uns alle, plötzlich auf eigenen Beinen stehen zu müssen? Und wie wird man von einer Weltmacht unabhängig, die jahrzehntelang die eigene Sicherheit garantiert hat? Holger Stark erklärt im Gespräch mit den Münchner Kolleginnen, wie und warum sich die politische Landschaft in den USA grundlegend gewandelt hat und welche Schritte in der deutschen und europäischen Politik jetzt dringend nötig wären. Wie sieht unsere Zukunft jenseits von Amerika aus? Hören Sie rein! [ANZEIGE] Mehr hören? Dann testen Sie unser Podcast-Abo mit Zugriff auf alle Dokupodcasts und unser Podcast-Archiv. Jetzt 4 Wochen kostenlos testen. Und falls Sie uns nicht nur hören, sondern auch lesen möchten, testen Sie jetzt 4 Wochen kostenlos DIE ZEIT. Hier geht's zum Angebot. 

Join Alison and TL;DR as we look at feminism, the only answer to the chaos of our times!

All the fun and insight of GameFace in 1/3 of the time! Reviews of Forza Horizon 6, Subnautica 2, and Directive 8020! Plus, PlayStation officially leaves PC, remakes of Halo 2 and Halo 3 are on the way, and much more!

Trail TMZ is back.Host and award-winning correspondent Allison Mercer dive into one of the strangest weeks of trail running that we have seen in a while. From the Satisfy and Adidas backlash and influencer culture debates, to doping discussions around Cam Hanes, Sage Canaday and clean sport, plus the growing role of social media in shaping running culture.We also talk FKTs, Will Peterson's Appalachian Trail attempt, upcoming Pacific Crest Trail action, why controversy dominates attention online, and whether running is losing the things that made it special in the first place.Topics:• Satisfy backlash and brand culture• Influencer running and authenticity• Cam Hanes, Sage Canaday, and doping conversations• Why negativity dominates social media• FKT updates and upcoming attempts• Will Peterson's Appalachian Trail• The future of trail running cultureSupport our Sponsors: Janji (code: Freeoutside): https://snp.link/a0bfb726CS Coffee: CSinstant.coffeeGarage Grown Gear: https://snp.link/db1ba8abSubscribe to Substack: http://freeoutside.substack.comSupport this content on patreon: HTTP://patreon.com/freeoutsideBuy my book "Free Outside" on Amazon: https://amzn.to/39LpoSFEmail me to buy a signed copy of my book, "Free Outside" at jeff@freeoutside.comWatch the movie about setting the record on the Colorado Trail: https://tubitv.com/movies/100019916/free-outsideWebsite: www.Freeoutside.comInstagram: thefreeoutsidefacebook: www.facebook.com/freeoutside#Trailrunning #Runningnews #Outdoors #Outdooradventure

This is a free preview of a paid episode. To hear more, visit www.theflyingfrisby.comThere's a lot more to AI than software. AI requires electricity, transformers, substations, cooling systems, data centres and more. That all means copper. Lots and lots of copper.Right on cue, the copper price hit fresh highs last week at $6.68/lb, before pulling back. So today I am going to take a long overdue look at copper. Was last week's action just a spike that will soon fade away? Or was it part of something much bigger? TLDR - the second one.Let's start with a 50-year chart to give you some historical context.Copper peaked in the great inflationary blow-off of 1980, before spending the next twenty years doing essentially nothing. The 1980s and 1990s were an age of globalisation, disinflation and cheap commodities. Who cared about hard assets or mining? Then came the rise of China and the supercycle of the 2000s. China urbanised, industrialised and turned itself into a superpower. Copper exploded higher, peaking in 2011. That boom then gave way to a long hangover. The 2010s were dominated by tech stocks. Mining died a death. To survive mining companies cut capex, reduced exploration and focused on balance sheet repair rather than growth. That decade of underinvestment laid the foundations of the shortages being revealed today.Meanwhile, while investors were busy buying software companies and meme stocks, the world quietly decided it wanted to electrify everything.The really striking thing about the chart is the speed of the rallies when they come. Then the amount of time copper spends going nowhere.Now here's the ten-year chart, with the one-year moving average in red and the 55-day moving average in blue. To my eye, copper appears to have formed a major bottom in 2020 during the Covid panic. The violent correction in 2022 increasingly looks like an early-cycle shakeout.Technically, the chart is undeniably bullish. Copper is trading above both moving averages, both of which are rising strongly. Momentum remains positive.That said, in the short term, the metal does look extended. Sentiment has become hyper bullish. Every investment bank now seems to have a copper supercycle note. Type “copper” into X and see what comes up: we are going to the moon on a copper superjet (powered by electricity natch).Now here's the three-year chart, to which I've added the 50- and 200-day moving averages and the RSI. The trend is your friend, and it is up.Historically, copper tends to be seasonally weaker over the summer months, and this is a spiky chart within its uptrend. I think we see some range-trading and consolidation over the summer months, which will provide something of a buying opportunity. But the charts are only half the story.The more interesting question is why copper may be entering an entirely new structural era.

This is a free preview of a paid episode. To hear more, visit www.theflyingfrisby.comThere's a lot more to AI than software. AI requires electricity, transformers, substations, cooling systems, data centres and more. That all means copper. Lots and lots of copper.Right on cue, the copper price hit fresh highs last week at $6.68/lb, before pulling back. So today I am going to take a long overdue look at copper. Was last week's action just a spike that will soon fade away? Or was it part of something much bigger? TLDR - the second one.Let's start with a 50-year chart to give you some historical context.Copper peaked in the great inflationary blow-off of 1980, before spending the next twenty years doing essentially nothing. The 1980s and 1990s were an age of globalisation, disinflation and cheap commodities. Who cared about hard assets or mining? Then came the rise of China and the supercycle of the 2000s. China urbanised, industrialised and turned itself into a superpower. Copper exploded higher, peaking in 2011. That boom then gave way to a long hangover. The 2010s were dominated by tech stocks. Mining died a death. To survive mining companies cut capex, reduced exploration and focused on balance sheet repair rather than growth. That decade of underinvestment laid the foundations of the shortages being revealed today.Meanwhile, while investors were busy buying software companies and meme stocks, the world quietly decided it wanted to electrify everything.The really striking thing about the chart is the speed of the rallies when they come. Then the amount of time copper spends going nowhere.Now here's the ten-year chart, with the one-year moving average in red and the 55-day moving average in blue. To my eye, copper appears to have formed a major bottom in 2020 during the Covid panic. The violent correction in 2022 increasingly looks like an early-cycle shakeout.Technically, the chart is undeniably bullish. Copper is trading above both moving averages, both of which are rising strongly. Momentum remains positive.That said, in the short term, the metal does look extended. Sentiment has become hyper bullish. Every investment bank now seems to have a copper supercycle note. Type “copper” into X and see what comes up: we are going to the moon on a copper superjet (powered by electricity natch).Now here's the three-year chart, to which I've added the 50- and 200-day moving averages and the RSI. The trend is your friend, and it is up.Historically, copper tends to be seasonally weaker over the summer months, and this is a spiky chart within its uptrend. I think we see some range-trading and consolidation over the summer months, which will provide something of a buying opportunity. But the charts are only half the story.The more interesting question is why copper may be entering an entirely new structural era.

Everybody has books, emails, and even text threads they intended to read but never got around to because it just felt like too much. So, you skimmed it. You got a sense of it. You can fake your way through a conversation about it. But it feels like so much work to dive in and dissect it. And that might be ok with a high school reading assignment, a text thread with golf buddies, or an all-staff email from HR. But what about a message from God, explaining the purpose of life and how to get the most out of it. Isn't that what the Bible is? Maybe. But where do you start and how do you make sense of it? Is it even possible for an average person to get something significant out of this ancient book on a daily basis?

People say you should read the Bible, but have you tried? Seriously. Ever sat down, opened it, started reading, and felt completely lost in under two paragraphs? What is this even talking about and what are you supposed to do with it? Meanwhile, everyone online is quoting it to and at each other, to support whatever personal agenda they have, trying to prove they're right and everyone else is wrong. Maybe you're better off just staying out of it. Can't the Bible just be used to back up whatever it is you already think, or does it have an ultimate point? And if so, what is it? If you're new with us, let us know how we can be praying for you, we invite you to fill out an online Connect Card by visiting https://southhillschurch.churchcenter.com/people/forms/91550—If you are looking for what is next for you, we invite you to fill out an online “Next Steps” card by visiting https://southhillschurch.churchcenter.com/people/forms/672517To give with us select the Give tab on the Church Center App or visit https://southhills.org/giving/ and select the Corona Fund or Corona BOW Fund—Visit our Linktree to find out more about everything mentioned in today's message or follow along with the message slides:https://linktr.ee/SouthHillsCorona —To RSVP for On-Campus Events select the Events tab on the Church Center App or visit https://southhills.org/corona/ TL:DR - Adam Smith “Left Unread” 5.17.26

All the insight and fun of GameFace in 1/3 of the time! It's GameFace TLDR! GameFace is back with special guest Stevens Charles from LS Cream! Star Fox for Switch 2 controversy, reviews of Saros, Mixtape, Invincible VS, and Aphelion, the Switch 2 price increase, and much more!

In this episode of the Founder's Sandbox, Brenda McCabe sits down with growth advisor and author Vanessa Golsby to explore what it really takes to scale private equity-backed SaaS companies. Vanessa shares the story behind her new book, The $100M Push: The Four Decisions PE-Backed SaaS CEOs Make to Deliver Growth in 100 Days, and reveals the four critical decisions CEOs must lead to build scalable, resilient growth: defining the ideal customer profile, aligning go-to-market execution, making strategic investment decisions, and creating long-term operational accountability. Drawing from her experience advising more than 100 middle-market software companies and serving as an operating partner in private equity, Vanessa offers an inside look at how investors think, why commercial alignment matters, and how CEOs can create predictable growth through disciplined execution. The conversation also explores the role of generative AI in modern go-to-market strategy, the importance of reputation and purpose-driven leadership, and the entrepreneurial leap Vanessa took to launch her own advisory firm. This episode is packed with practical insights for founders, SaaS executives, and growth leaders looking to scale with clarity, confidence, and purpose. You can find out more about Vanessa at: https://www.linkedin.com/in/vanessa-goolsby/ https://www.linkedin.com/in/vanessa-goolsby https://vanessagoolsby.com/ Or order her book at: https://www.amazon.com/100M-Push-Decisions-PE-Backed-Deliver/dp/1963549309 Transcript: 00:04 Welcome back to the Founder's Sandbox. I am Brenda McCabe, your host. Now in the fourth season, the Founder's Sandbox is a podcast that gathers business owners, founders, professional service providers. 00:31 and corporate directors. And we all are working towards the same mission, which is building scalable, resilient, purpose-driven companies to build a better world. We do this with underpinning, with great corporate governance, and really working with the founders to build that resilience and scalability. My guest, um join me here in what I like to consider a fun sandbox. 00:55 And this month, my guest, I'm actually delighted to invite Vanessa Golsby. Vanessa's joining me from, is it Dallas? Dallas, that's right. Dallas, Texas. So um more here, but thank you Vanessa for joining me on the Founder's Sandbox. And I wanna give a brief introduction to why Vanessa's here today. There's multiple um boxes that she checks, largely Vanessa. 01:22 has her own firm. She is a growth advisor who specializes in scaling private equity back middle market software companies. And it's an interesting time and that space that I'm certain we're going to get to a question here in a minute about the impact of generative AI and all those models out there and the effect on software businesses. You're a seven-year veteran as an operating partner. 01:48 in two private equity firms and portfolio SaaS CEOs. She has helped more than 100 middle market software companies drive growth, execute go-to-market companies, go-to-market, pardon me, turnarounds, and deliver investor returns through sharper commercial execution. That's all in the commercial execution, isn't it, Vanessa? That's right. Yeah. And prior to advising, she was a former operator leading product and commercial. 02:16 teams for 18 years at brands like Travelocity and Financial Times, which I didn't know that when we first were talking. I hadn't realized when we had our first conversations of your corporate experience with Travelocity and Financial Times. So you brought a lot of that corporate kind of know-how into the private equity world and you actually started your own firm. it four months back? 02:44 October, October of 2025. My goodness. So you're not even into your first year. I know. So, and, and, uh, you are an author. So your book, um, so I don't know when you found the time, Vanessa, but your book, the 100 million push the four decisions PE backed as SAS CEOs make to deliver growth. And a hundred days is out. 03:13 Matter of fact, this last week and we're in the third week of April, it uh hit bestseller, right? That's right. Amazon. Yeah. And in that book, we'll get into it. You distill the framework that you've developed. I don't know when, while setting up your own firm, but you developed over decades in the trenches, codifying the sequence behind the big four decisions. 03:40 that enable CEOs to scale with speed, clarity, and confidence. So welcome to the Founder Sandbox. Great. Thanks for having me. Happy to be here. Well, I always like to start with uh my guests to really talk about your origin story. And I think what's very appropriate for today's uh episode is what drove you to actually write a book, right? 04:09 because it distills both your professional as well as um this new tool that you got out there in the market. Yeah, you know, I never thought I would set out to write a book, if I'm being honest. I had, I'd spent, at this point, I'd spent probably about five years as an operating partner, so as a growth advisor for PE firms. And so in that role, I had been 04:38 pretty well practiced at writing best practices. So I understood how to codify a framework and explain it, you know, in long form, basically. But I never had dreams of being like a full author, like writing a book is totally different than writing a best practice. uh But a really strange thing happened about five years into my career as an operating partner. So I'd had about 18 years, as you mentioned, like in the trenches, like a tactical, and then about five years as an advisor. 05:06 And um over the course of those five years, I had developed for myself this framework because when I moved to the firm that I was at at that point, I was having to work on about 10 software companies at a time. And it's really difficult to show results uh efficiently when you're having to focus on so many different companies who have different industries and different sizes and different needs. And so I created this framework just so I could work at scale. 05:35 And uh I had been running it probably about three years at this point when I needed to go back and take a look at some of my case studies. So I wanted to collect case studies. And luckily, because I was still at the firm, I was able to get access to actual data from these companies that had been running the framework. And oftentimes what happens, because I focus on middle market software, there's a sales cycle. So oftentimes what happens 06:04 is we'll run through this framework and we'll see immediate results by way of pipeline and maybe bookings depending on the sales cycle time. But oftentimes we don't see the actual bookings and revenue results until a quarter or two after, depending on what it is that we're selling. So this was really the first time that I had really paused and like done, if anybody here has had to do a case study or fact finding exercise for a PE firm, know like what a... 06:32 slog it is to have to like go look through all this data. I like found the time, I prioritized it. And what I found was, I mean, there was no surprises in terms of like when we wrapped up our, usually my engagements, I try not to be there longer than 90 days. So it's either a 30 day, 60 day or 90 day plan that we run through. It's pretty tight ah in terms of how we manage through it. So by the end of our... 06:57 I have a sense of some results, like whether it's pipeline or early bookings. have some walking away knowing that we've seen some lift, but this was the first time I'd been able to go back like a couple of years to see like, what about those first companies that ran through it? And I'll tell you, Brenda, I fell out of my chair. I was like, I cannot believe the consistency. You can see in the data, like the trajectory, the upward trajectory from when we started working on the framework and then where they were today. And 07:27 At that, that was like the first seed. Like that was like a Thursday. And I was like, I don't know what to do with this information, but I have this information. Oh my gosh, this works. can't believe it. Right. And I really had to sit with that. And over the course of like two or three weeks, a few other things kind of happened that led me to the path of writing a book. Um, and one of those is I was listening to a podcast. I'm an avid podcast listener. 07:54 And I was catching up on April Dunford. She wrote a book on positioning. Obviously awesome. It's a great book for positioning. And I was going to have to run a positioning workshop. And so I was like, oh, let me like get into my head back into the game on messaging. So I just like queued up like the latest podcast I could find from her and then went on a run. And then I was like a captive audience. I went on this run. It turns out the podcast I had queued up was not about positioning. It was about her journey as an author and writing her book. 08:23 So I spent an hour listening and getting really inspired. And when I came back from that run, I thought, you know what? I have to tell the people, there is a way to consistently build and scale companies when they're going from, my framework is very from 10 to that first 100 million. And so that was really the inspiration for me. then it's just been a journey from there. 08:52 We'll get to it, but you uh codified um when you had those aha moments, right? You went back and looked at the cohorts of the companies that you had been working with, right? 30, 60, 90 day framework, for lack of another word. Can you share what are those four things that enterprise SaaS CEOs do? 09:18 Sure, so my framework is an order of operations. So everything that happens at the beginning has like downstream implications on the other activities. And originally when I created this order of operations, I hadn't high leveled it in terms of four decisions. I did that for the book because I wanted to write the book for CEOs. CEOs are such a, especially going to the first hundred million. CEOs. 09:45 have to have their hand on the strategic wheel of commercial growth. not yet mature, they haven't yet matured out of that. There is a place over a hundred where you can start to delegate more of the idea of commercial strategy to like a, you know, top tier executive CRO, for example. But when you're working on the path, especially if you're PE-backed to a hundred, you really need to stay involved. And that had, I had noticed that that core ingredient oftentimes was 10:15 one of the gaps I was inadvertently closing when I was working with these companies. And so because of that, I wrote the book for CEOs. And since I was writing it for CEOs, I was like, oh, I need to go one level higher than my traditional order of operations, which is very like activity sequenced and like talk about more of like, what is like, what is strategy? Strategy is making a decision and committing to it. So what are the four decisions that a CEO needs to direct and commit to have their team commit to in order to see this growth? 10:44 And those four decisions kind of tell the story of growth from up to the first hundred million. Frankly, it's kind of the same above a hundred, except the last decision actually becomes the first decision over a hundred. But anyway, that's right. So four decisions that CEOs that you were saying that are 10 and get to and to get in order to get to a hundred million, they have to be really continuously involved. 11:13 in the growth of the company. They cannot delegate until they reach that um upper level. They don't necessarily need to direct or be boots on the ground in these areas. But when they make these decisions and they guide their teams and champion these decisions, what happens as a byproduct of this is they inadvertently align their business in a way that is 11:43 successful for commercial strategy. So for example, I'll just walk through the decisions quickly to give you an example of how this works. um So the first decision, I high level it as the ideal customer profile or the ICP, which is just another way of saying who are we going to target? And my bit, my specialization is being PE backed. So part of what CEOs and companies hire me for is certainly the pattern recognition of working on over a hundred software engagements. 12:13 but also that sort of behind the scenes view of what the investor is expecting. you know, bringing that idea. When your PE backed, once that investment round closes, are inadvertent, not inadvertently, you are inherently um signing up to expand and grow either within your market, into an adjacent market, or in some other capacity. And just by that definition, you need to, 12:41 understand who your target is going to be, who your best buyer is going to look like for this next round of growth. So it's generally, this is such a major trigger event, this idea of becoming um PE backed, that it's generally a signal for CEOs to say, okay, now let's take a look and see if our existing customer today is going to get us to where we need to be in five years. Because that's five year journey is what you've signed up to take on essentially. So the first 13:10 The first decision is that ICP decision. Once we have an understanding of who we're going to target, then we focus, especially with the commercial side, we focus on how are we going to turn those targets into opportunities, right? So in software, it very much goes from like lead to opportunity to closed one deal, right? So that's what I mean when I say opportunities and or pipeline opening. And this idea of how do we turn targets into opportunities? I high level this decision as the SLA. 13:40 which is a pretty common service level agreement. in this framework, it covers about five or six very specific decisions that your sales, marketing, channel partner and CS teams need to align around to ensure that the build of their lead management system and how they're qualifying those leads to become opportunities is sufficient enough to have some predictability. like you have some confidence that when you put a dollar out, 14:10 into a marketing campaign, it's going to convert into pipeline, really, right? And then ideally into bookings from there. And so that's the second decision. the first one, who do we target? ICP decision. The second, how do we turn those targets into opportunities? The SLA decision. Once you reach... 14:29 Once you have the confidence and some predictability flowing through, now you're ready to make a more strategic decision. And these last two decisions are really where the CEO not just champions, but takes an active role in the decision making. The next one is the contribution decision. So this is now that we know who we're going to target and we understand and have confidence that when we target those buyers, they are going to turn into customers. The next question is where do we invest? 14:57 to go get more of those targets. So who's going to contribute to our revenue number? How much are we going to put into channel partners? How much are we going to invest into marketing? How much are we investing into outbound? How much are we investing into PLG or a self-serve motion, right? How much is new? How much is expansion? And in this decision, we start to bring the CFO in to take more of a governance posture around commercial. So we give the CEO more context around 15:26 Some of the horse trading that typically happens in a silo between the teams. We now have those kinds of conversations around investment decisions and headcount and budgets all together in a room. I run this like a workshop, but all together in a room. And the book teaches the CFO and the CEO how to run this on their own. Excellent. for kind of the terminology that I would use and correct me if I'm wrong, it's kind of capital allocation. So a bit more rigor. 15:56 is brought in with this discipline of budgeting, right? You're talking about contribution decisions, So it's budgeting, capital allocation, and um bringing another uh kind of the controller of the purse strings, the CFO. That's right. Right? And jointly with the CEO are posturing and actually sprinkling it down to their direct reports, I suspect. 16:25 Right. Well, we so the way that I teach contribution modeling is everyone needs to be in the room. No one function, not the CFO, not the CEO, not the CRO can make these decisions for the entire commercial team who is actually going to need to. Yes, it is a budget allocation exercise, but actually that's the second step. The first step, it's a goal setting exercise. oh We break down. 16:53 Each of those pipeline sources has different stages, which we just got very deep on in our SLA decision. So we understand what those stages are called. We understand how long we expect somebody to stick in those stages. We understand what those conversion rates are through those stages. And now that we have some sense of those inputs, we basically enabled ourselves to sign up for a number. So now we can look at marketing and we can say, oh 17:22 If you're gonna sign up for a million dollars in pipeline this year, that means at this selling price, you're gonna drive this many deals, right? At this conversion rate, at this close rate, this means you need to have this many opportunities and that this conversion rate from lead to opportunity, you need to drive this many leads. Can you drive this many leads? And the marketing person's like, that's a lot of leads. I don't know if I can drive that many leads, right? 17:48 And if they hesitate and they say like, can't realistically get that many, we look around the room and we say, okay, who else can drive more leads? Let's look at channel partners. Now we do the same thing from referral to meetings booked to, know, et cetera, et cetera down the So it's very like, it's very precise in terms of setting goals at the funnel stages, but not to become that, like we're not expecting frankly, to get a bullseye out of this workshop. What we're doing is we're kind of snapping the chalk line to say, 18:17 Okay, this is what we think we can go do. And now we're gonna meet with the CFO leading, we're gonna meet every two weeks or every month, and we're gonna see how we're doing. Are we driving this many leads for marketing? Are we getting this many referrals from channel partners? Are we booking this many meetings through the BDRs? And if the answer is no, then we look around the room. Where else can we do it this month? So we have something we can react to in real time, and rather than showing up to the board meeting and saying like, yeah, it was kind of a miss, but I think we have some ideas for next quarter. 18:46 Like this puts everyone in a position now to become far more reactive to what's happening in real time uh as a group, as like a singular one team. And what about the fourth? Yeah, so the fourth decision. And again, this decision is fourth when you're going to 100 million. But if you were above 200 million or as you like progress to like four up to a billion, this actually can become sometimes the first decision. 19:14 when you kind of need to work your way to this point um for when you're going to 100 million, especially after the contribution decision, that contribution. Yeah. Cause that's going to surface a lot of ahas for teams. Like oftentimes you're like, Oh, actually we need to break into a new market. We're saturated or, my gosh, you know, like we need a, you know, too many, we need a ton more reps or actually we don't need more new sale reps. What we need is expansion reps and really need more there. So 19:43 Like in that contribution conversation, you really surface so many of your growth levers that you're prepared for the fourth decision. So the fourth decision is now that we know who we're going to target and we know with confidence how we're going to turn those targets into opportunities. And we understand where we're going to investigate more of those targets. Now we talk about how are we going to do this over the long term? So how are we going to do this not just this year, but for the hold period? So for five years. 20:10 And so this decision I high level as the OKRs, which is an industry term. I didn't come up with that, but it stands for objectives and key results. And it's essentially gives the CEO like almost like a project management framework for long-term planning. um And you really can't necessarily jump to number four if you're going up to that hundred day plan without having these first three decisions at least somewhat cemented or somewhat committed to. 20:39 um Otherwise, what ends up happening is your OKRs are, you know, have like 25 things you're going to try and go tackle. So you kind of like, kind of, you know, by just by um the effort of making these first three decisions, you've already like started to prioritize for your team where the important levers are that you're going to focus on. 21:01 Thank you. I wanted to ask you by publishing this book, are you putting yourself out of business? That's a good question. A grow-to-market advisor, The enterprise SaaS sector that's under a lot of pressure right now with the dinner to bay eye. So let's take the two questions. Let's take them apart. And I'm being a bit. It's a great question. I asked myself that question. Yeah. 21:29 Yeah, my publisher asked this too. Why put it out there? You're putting yourself out of business or no? Yeah. Well, you know, the way I, there's a couple of answers to this, a couple of dimensions to this. The first is, you know, a lot of the motivation to write this book was to get the word out. Like when I saw the consistency and how well the results sustain when companies run through this framework, I was like, Oh my. 21:56 Why aren't we telling all of the CEOs that there's a way to go do this? Like we know these activities, it's things like territory planning and quota setting and SLAs. like, know, people know that activities that need to happen, but the unlock here is the sequence, like it's important to do them in order and that they're done altogether, which is the role of the CEO, right? Is to ensure that the right people are in the room when you're making these decisions and everything's like. 22:24 That's the those are the connectors right is are the those are the interlocks are the decisions the activations happen You know within the function so I? Was passionate like we talk about purpose the reason I was excited to be on this podcast is because this is very purposeful for me It felt like holy cow Look what I discovered under the pyramid I got to tell the people like there's an easier way to do this We don't have to bang our head against the wall to try and figure this out the hard way so 22:53 In that way, it didn't really feel like an option to necessarily hide it. ah And then the other side of me thought about it in terms of like changing the oil in my car. Like, I know that I can change the oil in my car. It's not a difficult, complex process. Like, it's very straightforward. But do I want to do, do I want to like get in coveralls and crawl underneath my car, like find the little lackey thing? No, I don't want to do any of that. I would far rather just bring someone in. 23:22 take the guesswork out, have it done, have it done correctly the first time, and leverage someone else's expertise in case they find something that I wasn't expecting. ah So I feel like I'm still bring, like when people leverage me to run through this, I'm still bringing a lot of value that you're not gonna necessarily get out of the book. mean, people, CEOs and firms hire me because of the pattern recognition and because I've seen these things play out enough times across different industries. 23:51 uh But I don't want to be a holdup. Like, please, if you are able to do it, then I welcome, I encourage you please to go run these plays yourself. And I try to give a lot of, it's very structured. This book is, the structure of this book was really difficult to come up with. It probably took me the longest amount of time, honestly. But I wrote it in a way that a CEO could read it quickly, because I know they don't want to read too many things. They are very busy. um 24:18 And so like they could digest it quickly and they could hand it off because that's kind of their role is to say like, I'm going to now equip my leaders to go do this and do it successfully. And they still have a role to play. But again, they don't have to be like in the trenches. Right. And without um seeing the book right now, I sound and Kendall on audibles or Kendall, um are there like exercises? Are there, is it like a handbook or is it um I'm a CEO? I 24:48 read your book um and I want to contact you. Do I to come in and maybe do some seminars? How does that work? Because this is a marketing tool as well. Yeah, yes. mean, of course I this book can be just a step by step guide for CEOs and their teams if they want to take it that way. So I tried to write it dimensionally. So the first dimension is 25:13 It equips the CEOs to understand, like the first two chapters are really around what is the investor expecting of you? Basically it's like, here's a little bit of the behind the scenes. Yeah, that was intriguing for me when we first spoke of it. Yeah, you've been in that room. Yeah, like I've been in it. Yeah, exactly. like, you know, one of the things that, again, like a lot of things happened in this like two or three week time period when I was kind of coming to the conclusion that I was going to write this book. And one of them was I was in a board. 25:44 meeting and there was a CEO advisor also in this board meeting and I could see the CEO advisor was um giving great advice based on their singular experience but the truth is is their experience was so unique to them that it would be really difficult it'd be like saying like 26:07 Yeah, just, once you press post, it's gonna go viral. It's like, let's not over promise here, you know, what's realistic. And that really hit me to say like, oh, this is a unique perspective. Like I'm not necessarily an investor and I'm not a CEO. it's been years since I've like managed a commercial team or been a GM, but I have... 26:34 I've flown all of those altitudes and I've been an observer in all of those rooms so many times that like the patterns, you just can't deny the patterns. um So yeah, I'll stop there. I'll pause there. So you do the reveal, right? So for any CEOs of enterprise, um SAS companies, this is a must read, right? Because you're doing the real deal. What is actually happening in the boardrooms of those private equity? uh 27:05 partners right that are yes looking at their portfolio companies yes yes thank you yes so i start with like you need to equip yourself with understanding what is expected of you when you took this investment which isn't frankly always talked about like it's not always revealed to the CEO ah so that's the first step and then it is a step-by-step guide so like there are the four decisions and then within each decision 27:33 I show them the book is structured to show them, tell them what the decision is, give them some case studies of other companies who have solved it, give them some red flags that say like, look, this is a really helpful book if you just closed your investment and you need to run like a, they call it a hundred day plan of like, you're going to deploy a lot of that, those investment dollars very quickly in order to like try to get traction on growth. So this is, I wrote it in that framework just because it is naturally 28:00 predisposed to running in like a 90 day plan framework anyway. um But it's also one that oftentimes in a hold period, you're going to hit some kind of plateau, right? It's very rare to like knock a home run out of the park right out of the gate. And so I also, so like in that, in that first part, so like each part, each decision has a part. So there's like a part for, there's like a four chapters on ICP, four chapters on SLA, four chapters on contribution. 28:26 The first chapter tells you, like gives you the red flags to look for if this is an issue, tells you what the investor is expecting, tells you your role and how you can direct the team, tells you when you need to maybe outsource, like what's the things you should absolutely do and the things that are kind of like nice to haves. Then the next chapter goes into how do you make this decision? And each of these decisions, the way that my approach is, 28:53 Um, is I like to do like 50 % gut and like 50 % data. So I always start my engagements with like surfacing from your internal experts already. Like a lot of times your C-suite lieutenants. Yeah. They like, I get called in for audits. Like that's like oftentimes I'm brought in initially for an audit of some kind. And in that audit, it's like a 360 commercial audit. And in that audit, I have like a week that I just cap off and I talk to anyone that you'll let me talk to. 29:23 And they're telling me the problems. like, this is really like, we've known this is very rare for people to like, I have no idea. They know what they did to get here. And so we start with the gut. And so in this framework for the book, the gut is surfaced through workshops. I'm a huge advocate of workshops. think, you know, honestly, my time with Vista really beat this into me, like the importance and the value of workshops, because not only is it a great place to surface everyone altogether, but it's 29:52 early adoption. Like when your voice is heard and you could challenge something in the room, when the decision is being made, you're far more likely to adopt it when we get to the final output. So I'm a huge fan of workshops. So each of these has a workshop. And this is a lot by and large when I'm training, when I'm teaching the CEOs, it's like, this is what you need to get out of the workshop. This is agendas. You can, have all of my agendas are up for download. Like you can download the agenda. You can run through it yourself. And this is who needs to be. 30:21 Yeah, like I want this to be helpful. That's the whole point is like it's supposed to be taking the guesswork out for the CEOs. uh And then you need to there's a data validation. Like, yeah, everyone's got gut. But then we do need like we are going to make some commitments here. So exactly. Yeah. So we need to like in each of these have different places that you go and source that data to validate. uh 30:43 So that's how we make the decision. Then I go through how you execute the decision. And for CEOs, this is almost like the TLDR. It's like, give you like, look, these are the steps that they're go through. Then in each of these chapters, I go far more into detail. This is what you're gonna go tell, like this is what your management team is gonna go do. And this is what good is gonna look like. So you're not done with this step until you've seen these five things come out of this exercise, essentially. 31:07 And then finally, each of these parts, so we've got like, what is the decision? How do we execute the decision? I'm sorry, how do we make the decision? How do we execute the decision? And then how do we measure the decision? And this goes back to how your growth story. So a CEO's role is not just to understand, right, our long-term objectives that may be surfaced in our investment thesis, right? Those are the first two chapters. It's not just coordinating the execution and setting the priorities and resourcing your team, right? Those are the four decisions. 31:37 But you also need to tell that story and you need to tell it in a way that makes you show well, that makes your company show well, and that makes you more attractive, frankly, at your next round of investment. so, yeah, externally telling exactly. So as well as internally. that's right. So that was really long winded, but that's basically the structure. It goes pretty far into detail, but I do. 32:02 high level for CEOs, like you can skip this part, just give it to your zero. So, so the book is out and um you started as you went rogue yourself and said, I'm working for myself and yeah, that's right. And um what happened is you've got some of your clients that had seen your, your work in prior years and, have taken you on as their advisor. 32:31 Why are they taking you on? it around your, are you scalable or your purpose? I mean, you're wanting to give back. So yeah, tell me. And you shared a little bit when we were talking before the podcast about you got a call from a client that you had from many, many years ago. Yeah. Yeah. I, you know, when I was deciding to go out on my own, it was really scary, right? Because I had, I never really even, I, I had been motivated to write the book. 33:00 And that was almost as far as my thinking had gone. And then at that point, the book was supposed to come out. Originally, the book was supposed to come out in January and we could have a whole other podcast about writing a book. so originally it was kind of, I knew like internally, I was like, gosh, by October, I was like, I need to make a decision. Like, what am I going to do? Am I staying? Am I going? Am I doing something else? And so I reached out to every person that, that I, you know, had some sort of like respected conversation, like a respected relationship with. 33:29 over the course of my career. And I basically asked him like, what do I do? What would you do? And I'm really lucky because at this point, I had been an advisor for about seven years, you know, with really established firms and the folks that I had worked with, that knew me, knew what I could do, had since gone on to a million other firms. So like my network on the firm side was pretty large. 33:59 And in those conversations, there was just inevitably a conversation that ended with like, look, if you go, I'll give you your first client right now. And so I was like, well, there you go. Close the door, a window, let's go. That was how it went. Yeah, so you reached out to your network, which is super powerful. Yeah, it really was. And it was honestly, I had surfaced my network throughout kind of writing the book because 34:27 You know, one of the things I think that is unique about my situation versus some of the other authors who have written fantastic, and I'm an avid business book reader, Fantastic Frameworks, is that my perspective is from the operating partner's point of view. And I am, yeah, it's very like, and so I'm really lucky because I, as I mentioned, like a lot of the folks that I have worked with over the years are now at so many different firms. 34:57 And so as I was writing this book, I would send out surveys to people and just say, Hey, just like gut check, do you see this too? Are you seeing this? Like when I wrote a whole chapter on like the value creation plan and you know, the value creation plan is one of those things that people talk about. Like it's this like standardized formal process, but it's wildly different, like firm to firm, like it's so totally different. And I just wanted to uh get a better sense of how these different firms of these different sizes were actually running their value creation plans. 35:26 And that's just impossible for me to do by myself. Like I need my network for that. So this whole process has been really great. And just like also bringing together some of my work friends that I hadn't been able to really, or I hadn't like, you know, kept up with as well as I should have. And so now I feel like my network is just like really thriving and humming. And I feel so much closer to like these people now than I have in a long time. So it's been really beautiful in that way. 35:54 Thanks for sharing. know, I want to ask you how has, well, your frameworks be at all affected in your opinion by the generative AI and how it's taken quite a bit of value out of the stock market. So now it's back up, right? So let's, so was, are you isolated from that effect? Your, your, your, your, just your, your frameworks. 36:22 Yeah, you know it's funny I wrote this book so I've done a lot around writing best practices for AI for go-to-market teams so I was pretty what by the time I wrote this book I had a lot of already like pretty packed research and thinking around AI and what it could do and what it couldn't do. I of course how could I you know I wrote this book almost two years ago now like 36:46 has really changed the game and just some of the new models that have come out. We knew that they were gonna be pretty revolutionary, but it was hard to be very specific. But I did, in the book, I have a very specific point of view on how AI can ah make what you do more effective, more scalable, where can use what you are bringing to the table and... uh 37:12 The word is escaping me, which is ironic scale, basically what you could do. And so that's my approach to AI and it's still my approach to AI. So I don't see AI as a competitor. I see it as an accelerator, really. And so I'll take account scoring as a great example. So in this idea of 37:38 these four decisions, one of the activities that you inevitably will need to do, it's under the ICP decision. So once you have an understanding of who you're going to target, you want to then score the accounts that are in your database to say like, is this a tier one, is this a tier two, is this a tier three, is this a tier four, and we're not gonna like, they're actually gonna churn too fast for them to even be worth that selling to. And so you're building out this account scoring model. Now, there are platforms that can just do this for you. 38:06 and they're just like, look at your data and they're like, great, we're gonna do this for you. But those platforms don't know your growth plan. They don't understand like what your investment thesis is. They don't understand that you have a very concentrated point in time where you're going to make, you know, a 30 % CAGR, you know, you've got like big, big goals. You're not just trying to do status quo every year. And so it's in that same kind of vein, like the human still needs to drive and be the director of... 38:33 where the AI is going to execute. um But AI is a fantastic accelerator. I'm excited. I love partnering with AI. It's not perfect. I think of it as almost like an MBA intern, like whip smart, smarter than I will ever be. But you can't totally take your hands off the wheel. You're like, there's context. That's great analogy. Oh my goodness, that's hilarious. It is true. um 39:03 AI. particularly like the perplexity model because it's on top of all of them for uh writing and preparing some of the work I do with my clients. So it becomes my companion is what I call it. Right? Yeah. Oh yeah. Definitely. Excellent. Well, I'd like to give you an opportunity to share how my listeners can reach out to you. Oh, sure. They'll be in your notes. Vanessa. Carry on. Okay. Great. 39:32 So I have a website Vanessa ghouls be calm I'm also on LinkedIn both ways You know are pretty easy ways to just you can look at my calendar and schedule time if you're interested Often time like my most most of the ways that I get brought into engagements is There is some kind of trigger event where the CEO or the PE firm Says like we need we need some 39:59 things, some kind of audit, some kind of assessment, some kind of strategy, some kind of like, what are our growth levers, right, to get us to whatever the next thing is. It's generally a two to four week audit. em And as I mentioned earlier, it combines interviews with your team with I have like a list of artifacts that we start off with. It's, I don't want to say it's like diligence, because it's not like diligence. But it is a pretty thorough 40:25 uh So you get sales, marketing, customer success, channel partners, digital, all of that. uh And oftentimes CEOs will have like a specific need on top of that. you know, I've got one where I just did one where it was like, we want to see, you know, we know we just got our investment came through and we kind of need to set our hundred day plan. So where should we go? You know, what are the foundations we need to build and fortify for this next round? uh We have one. 40:53 One other trigger that's pretty common is on the back of maybe M &A, where you have like two go-to-market teams that need to integrate together. Yeah, they like will bring me into sales. How are we gonna do that? Yeah. Or they have done that and maybe they're still not quite hitting that like expansion number that was originally conceptualized. um And then, yeah. And then the third, which is, I mean, it's like the... 41:21 the least positive, but honestly, the most exciting for me is, you you're like an a mid hold plateau. You're like, gosh, you know, I had one just last month where it was like, they hit this $30 million ceiling and they for like three years have thrown every spaghetti they could at the wall and just could not get past this ceiling. And, um, and so like the audit can, it's very focused and like trying to get to whatever the objective is, but it's, it's holistic because my whole, my whole shtick, right. Is that like, 41:51 It's no one team. It's like all of the teams kind of have to interlock in a line together. Yeah. Yeah. Quite revealing. Excellent. Those are excellent use cases. Um, and we'll put this in the show notes as well as your website and Vanessa. Um, let's come back to the sandbox. I do like to do a round of just questions about three words and what is the meaning for you. Um, and each of my guests comes up with their own um interpretation, their own meaning. it's 42:19 So what does resilience mean to you, Vanessa? Yeah, think resilience means being internally motivated. There's a drive that is not necessarily anchored or reactive to anything that's happening externally. uh For some reason, you just can't let it go. 42:47 How about scalable? What's scalable? Oh, wow. I mean, spent so many years uh writing about being scalable. Yeah, you know, it's funny when I think about being scalable, you know, it actually initially comes to mind as like growing pains, like this idea of growing pains. uh And I'm just now kicking myself for not reading the prep questions closer. We're going to rip a little bit, but. 43:15 But yes, being scalable is having that resilience through the growing pains, knowing, right, having like some kind of faith that at the end it's gonna be bigger, better, probably bigger than you even really could even have imagined or maybe even in a direction that might not have been initially planned. Excellent, excellent. Yes, and I also wanna just, I think. 43:43 you know, we're back to the title of the episode, is, um, and which is building purpose, building reputation with purpose. And you were adamant about that. So what does purpose mean? And maybe you'll bring into, know, what, what is building reputation with purpose for you? know, I, um, 44:11 It's funny, I feel like it really goes back to this resilience question, but it's so much of it just comes down to acting with kind of like, like I work with companies that have like cultural values, right? And they're like, oh, or Patrick Lindsay only has a great one, like the heat, likes to say, you know, hire people that are hungry, humble and smart, right? So like, you have your like keywords, your brand words, your value words. And I think for me, 44:40 um over the years, my purpose has been to act with integrity and grace and curiosity. And, um and that's something that I don't think about logically, right in life. But I try to bring that kind of inspiration to the teams that I'm working with. And it's a lot of the reason why I wrote the book was to say like, 45:10 Look, there is a way. You don't have to follow every single thing that's in this book. But if you get stuck, isn't it helpful to have a guide, like a troubleshooting guide to say like, oh, let me just go to the index here. I'm a little stuck on territories. I'm going to get over it. And that's the spirit that I try to bring to everything that I do, which is, yeah, we can solve any problem. Like any problem is solvable. And guess what? Execution problems are the easiest thing to solve. So like, 45:40 Let's have some fun and we can, we can, there's a way to do it basically. Right. Excellent. Thank you. And last question, did you have fun in the sandbox today? I had so much fun. This was great. You know, honestly, I didn't really know how this, like I do enough of these podcasts now and it's so usually anchored on the framework and like, you know, the execution and like, you know, very tactical. 46:07 And so this was just a really, this was like a breath of fresh air because we got to talk a little bit about the human side of it, which I find really motivating. It is. And I do recall you were really set on building you and you it's your reputation. Do you have Vanessa Goldsby that has gotten to you, gotten you where you are today and by giving back and providing that, you know, writing that book and then, you know, serendipity, you decide, Oh my gosh, I'm going to go out on my own. So it's, your reputation. 46:35 that has been built with purpose. I want to thank you for joining me here in the Founders Sandbox. To my listeners, if you like this episode with Vanessa Goldsby, sign up for the month release of the Founders Sandbox where I have guests that are Founders, business owners, service providers like Vanessa, um and board directors who build with strong governance, resilient, scalable, and purpose-driven companies. 47:03 So signing off for this month. Thank you very much. Thank you, Brenda.

Kneecap joined PoliticsJOE, fresh off the back of having a terrorism case, against member Mo Chara, thrown out of the British Court. It's also the premise of their new album, which they discussed with our very own Irishman-in-residence Seán Hickey. If you somehow missed the trail of the UK and Ireland's most controversial musicians, here's the TLDR. Kneecap were involved in a UK court case because member Mo Chara, real name, Liam Óg Ó hAnnaidh was accused of displaying a Hezbollah flag during a concert in London in November 2024.UK prosecutors said this could amount to an offence under the UK Terrorism Act because Hezbollah is a proscribed terrorist organisation in the UK. Prosecutors also alleged slogans supporting Hezbollah and Hamas were shouted during the performance.The case was initially thrown out because prosecutors failed to get the Attorney General's approval within the legal six-month time limit required to bring the charge. The Crown Prosecution Service later appealed, trying to reinstate the case.In March 2026, the High Court rejected that appeal, meaning Mo Chara didn't face trial. The judges said the prosecution had been started unlawfully due to the procedural error, not because the court ruled on whether the alleged conduct itself was legal or illegal.Kneecap argued the prosecution was politically motivated and linked to their outspoken support for Palestine. A media carnival, if you will. Hey, that's the name of one of the songs on their new album. Subscribe to How to Rebuild Britain now: https://linktr.ee/howtorebuildbritain Hosted on Acast. See acast.com/privacy for more information.

Join Alison and TL;DR as we look at feminism, the only answer to the chaos of our times!

Steve and Dave Jolliffee realized that driving ranges lacked feedback. Golfers need feedback to improve, so they created a way to get feedback. Dave Young: Welcome to the Empire Builders Podcast, teaching business owners the not so secret techniques that took famous businesses from mom and pop to major brands. Stephen Semple is a marketing consultant, story collector and storyteller. I’m Stephen’s sidekick and business partner, Dave Young. Before we get into today’s episode, a word from our sponsor, which is, but well, it’s us, but we’re highlighting ads we’ve written and produced for our clients. So here’s one of those. [Seaside Plumbing Ad] Dave Young: Welcome back to the Empire Builders Podcast. I’m Dave Young. I’m sitting here with Stephen Semple. Well, actually, I’m not sitting here with him. I see him. He’s on the screen. Stephen Semple: We’re virtually together. Virtually. Dave Young: This is an international podcast. Stephen Semple: It is, actually. It is actually very good. Dave Young: By the way, I know I think the He-Man episode has dropped. Stephen Semple: Yeah. Yeah. Dave Young: The artwork that Matt Burns or whoever did it for that one. Stephen Semple: Yes. Matthew did it. Dave Young: Yeah, that was great. He sent me that. Stephen Semple: Actually, the one that I really like was the one that he did for PT Barnum. I thought that that was fun, where you’re the guy hocking the tickets. Dave Young: I haven’t seen that either. He sent them all to me. For some reason, he sent that one to me. I should probably subscribe to this podcast and listen to it. Anywho, Steve. So, Stephen whispered in my ear the topic that we’re going to cover today, and it’s actually one that I sent him. Stephen Semple: You actually whispered in my ear. Dave Young: Yeah, I did. I kind of did. I was surprised that you were ready to do it. And it’s the story of Rose Blumpkin and Nebraska Furniture Mart. She’s one of my local heroes back in my home state in Nebraska. Stephen Semple: Sorry, that’s not the one we’re going to be talking about. Dave Young: Oh, sure. Stephen Semple: You sent me another idea. That one, I’m going to do, but I don’t have all the research on it. Dave Young: Okay. So we have to start all over. Stephen Semple: No, or we could just keep going with this, Topgolf. Dave Young: Oh, right. Yeah. Stephen Semple: You also sent me… You want to just keep going with this? Dave Young: So now people know that we’re going to talk about Rose Blumkin at some point. Stephen Semple: Yes. Yeah, sure. Dave Young: That’s a cool story. You just want to make me look as scatterbrained as I really am. So, Topgolf. Stephen Semple: Authenticity. We’re just going for authenticity here. Dave Young: Yeah. So, Topgolf. I just thought we were talking about this other thing that I sent you. So yeah, I’d love to talk about Topgolf. So both of these businesses, since we’re not talking about Rose Blumpkins, I’ve been to each of them one time. Stephen Semple: Okay. Dave Young: My experience level is X equals one. Sample equals one size. Stephen Semple: Okay. Dave Young: But I saw the story about Topgolf or it was a video, wasn’t it? Stephen Semple: It was a video that you sent me. Yeah. Yeah. Yeah. It was super interesting and so much so that I ended up, as soon as it was done that night I had some time. I did a bunch of research, wrote it up and said, “We’re going to cover it,” because it’s a very interesting story. Dave Young: To me, if you want the TLDR version of it, the guys that started Topgolf thought they were in the golf business. Stephen Semple: Yes. Dave Young: No, they weren’t. Stephen Semple: No, they weren’t. Dave Young: They had no idea what business they were in. Stephen Semple: And this is the reason why I wanted to talk about this. So this is an idea, Dave, that you suggested to me. And when I looked into the history, there’s a couple of twists in this that I thought were super interesting that every business could learn from. And yeah, the story of this is that they didn’t really understand what business they were in. And when they found out what business they were in, they became super successful. And then the company that bought them lost sight of what business they were in and failed. It’s this crazy thing. So before we get into that, I want to hear about your experience at Topgolf because you went there and you’re not a golfer. Dave Young: No, I’m trying to think of who we went… It was a work thing I think. Stephen Semple: Perfect. Dave Young: And that’s typical, right? It was a bunch of us from work. It was actually a super hot day here in Austin, but they have giant fans blowing on you. Stephen Semple: Yeah. Dave Young: They’re the kind of business where, I don’t know. There’s lots going on and they’re pulling money out of your pocket all different directions. Right? Stephen Semple: At their heart, they’re a driving range. And it was started. The first Topgolf was built in Watford, England in 2000 by two brothers, Steve and Dave Jolffi. And these guys were golfers, tinkers, problem solvers, but they were not backed by big money. They were not from the golf industry and they were not tech guys. They were just two guys went to a driving range and saw a problem. Dave Young: Yeah. They’re like, “I don’t know where my ball went.” Right? Stephen Semple: Right. Start off with the problem of golf lacks feedback. So when you go to the driving range, you’re practicing, yet you don’t know how far the ball went, how straight it went, nothing, no feedback. Dave Young: Yeah. Stephen Semple: So over the course of three years, they played around and figured out how to embed a chip inside the golf ball. Remember, this is pre 2000, pre iPhone, pre all of that. The whole idea of something being able to track the speed of something really new, right? Dave Young: Yeah. Stephen Semple: You got to remember that. So the goal was to track every shot. The other thing they needed to figure out was targets that could take impact and speed and a system that translate this and the scores and make it durable enough to do it outdoors. So, 2000, Watford, England, it was ahead of its time. It was kind of cool and no one cared. Dave Young: Sure. Stephen Semple: Traditional golfers didn’t see the point. Non-golfers were not drawn in. It was not quite a sport. It was not quite entertainment. It stood for nothing. Being different does not equate into being desired. Being cool doesn’t equate into being desired. So they had to ask themselves this question. Why is this not catching on? So here’s what they did. They reimagined the entire environment and experience. They created covered base, so the weather didn’t matter. They put in lounge style seating, driving range. You can kind of all sit together. Dave Young: You could go with your friends. Yeah. Stephen Semple: Make it social. They put food and drink in. So people stayed longer. They put music in energy. It now became fun. Suddenly, it was a place to hang out. It was a night out. People came in groups. Non-golfers like Dave Young came. Dave Young: That’s right. And so you think about a driving range and it’s like going fishing. That’s interesting. It’s solitary. It’s, I’m going to go hit a bucket of balls. And usually what that means is I’m not necessarily working on my game, I’m just going to get inside my own head and process and think. And if you just do that on a park bench, people think you’re stalking. It reminds me of Thomas Edison. He used to walk down to the end of a pier near his office and sit on a bench with a fishing pole and he never had a hook on it. He would just sit there with a fishing pole and people would leave him alone because he’s fishing. Stephen Semple: Because he’s fishing. That’s interesting. Dave Young: Versus if you go stand on the end of a pier without a fishing pole and you’re standing there for hours, people will start to wonder what you’re thinking about, what’s your plan there. So driving range versus going with a bunch of friends to a “driving range,” two different things. Stephen Semple: Well, and here’s the other thing is a non-golfer doesn’t want to go out on a golf course because that’s hit the ball six inches, hit the ball. But to a driving range, sure, because it doesn’t matter. It doesn’t go anywhere you sit down, the next person hits. Dave Young: Yeah. It’s just a new version of putt-putt, right? Stephen Semple: Exactly. So this created this environment where you didn’t really need to know how to swing the club to have a good time. It turned a driving range into a social outing. It became entertainment, not sport. So enter Eric Anderson from West River Group, private equity company. Eric came across this idea and thought it was perfect for the US market. They invested a bunch of money in the company, took it to a new level with high-end restaurants and bars. And then in 2005, they opened a location in Texas. And it was a crazy hit. When it opened, there was hours long lineup. It was nuts. By 2010, it was a cultural phenomena. It was a hit with millennials. And this is a big deal because in 2010, golf is in a decline and it’s not attracting a younger audience. So suddenly, you’ve got this driving range, which is attracting all these people and it’s attracting millennials. In an October of 2020, during the pandemic, Callaway who had been actually an early investor in this, Callaway announces they’re purchasing the remaining 86% for $2 billion. Dave Young: And so here’s the thing, Callaway’s in the golf business. Stephen Semple: Hold that thought. Dave Young: Yeah. Stephen Semple: Hold that thought. Hold that thought. Yeah. No, seriously, hold it because yes, you’re right. Callaway’s in the golf business and Callaway’s looking at this and going, oh my God- Dave Young: Here’s the future of golf. Stephen Semple: … the group that we’re trying to attract. Here’s the future of golf. Not only did they buy Topgolf, they rebranded Callaway to Topgolf Callaway. And initially, there was a bump in sales of Callaway stuff because they had to also supply the clubs and the balls to these venues. And they were expanding the venues like- Dave Young: Why would they have any other clubs? Stephen Semple: Right. And they were expanding the venues like crazy. And they launched new products like quantum drivers and Chrome tour balls, things that were fun, right? Fun. Because they would put those fun things into the driving range, into the Topgolf driving range, and people would love it. So they thought this would be a fun thing to take to a golf course. Dave Young: But? Stephen Semple: Here’s the core problem. Dave Young: Stay tuned. We’re going to wrap up this story and tell you how to apply this lesson to your business right after this. [Using Stories To Sell] Dave Young: Let’s pick up our story where we left off and trust me you haven’t missed a thing. Stephen Semple: Here’s the core problem. Callaway’s in the sports business and this was an entertainment venue. Dave Young: That’s right. That’s right. Just because I went to Topgolf once doesn’t mean I ever went back to a driving range. I used to golf. I used to golf. Stephen Semple: Right. Dave Young: And a driving range was always… It was a good introverted experience, but I wasn’t any good at golf. And I bowled for a while too, and I quit both sports the same year because my average was the same in both. Stephen Semple: So the bowling average was pretty good. Dave Young: You think about that, right? Low hundreds in both sports is no good. Stephen Semple: So the interesting thing, because again, initially, the numbers looked good, they launched all this stuff. It looked great, but beneath the surface, the synergy was actually a financial illusion because the core problem is Topgolf was an entertainment business. Topgolf was a restaurant and a bar with a driving range attached. Topgolf was not a driving range with a bar attached. Dave Young: I wouldn’t even- Stephen Semple: Millennials who came out… Yeah. The people who came out and swung a club, it did not convert into people going to the golf course and buying Callaway equipment. Dave Young: I think it’s a stretch calling it a driving range. I think it’s- Stephen Semple: Fair enough. Dave Young: It’s an electronic game that you use golf clubs and golf balls to try to score and win and play. Stephen Semple: Fair enough. Dave Young: I don’t think anybody… Well, maybe people- Stephen Semple: Would I’ve not been to one. I only saw pictures of it. Dave Young: Maybe people that go to Topgolf will go to a driving range to practice. I don’t know. I don’t know. The problem Callaway had was they weren’t converting people into actually going to a golf course. Stephen Semple: Correct. Dave Young: And buying golf clubs. Stephen Semple: Right, because they saw the business wrong. And what ended up happening, the business started actually declining because even the changes that they started making to the facility was through the eyes of a golfer and this being a sport, not this being entertainment. Now what ended up happening is when it all spun apart, Callaway ended up offloading the business to Leonard Green and Partners for basically a billion dollars. So they lost it. Dave Young: They lost a bit of dough. Stephen Semple: Well, they lost a billion on the purchase. And in addition, they had invested a ton of money in building these things out. This was a huge loss for Callaway. Dave Young: And so the thing that they misread was thinking that this was the future of golf and we’re going to sell a lot of clubs because of this. We’re going to sell a lot of clubs, all the things that we manufacture for the golf world. And I think of the golf world, and I think of almost like a white shoe law firm. I don’t think the people that spend big, big money playing golf or spending big, big money on Topgolf or vice versa. Stephen Semple: Well, there may be- Dave Young: If you’re a scratch golfer, you could go to Topgolf and clean up on your friends. Stephen Semple: Well, what I’m going to say is people who are already golfing would go to Topgolf because it’s fun. People who aren’t golfers who go to Topgolf are not going to suddenly become golfers. And that’s what Callaway thought was going to happen. All golfers will go to Topgolf, but not all Topgolf people will become golfers. And what I find that’s so interesting about this- Dave Young: Golf was the entry point for Topgolf, but Topgolf turns out is not the entry point for golf. Stephen Semple: Great. Dave Young: Yeah. Stephen Semple: So the thing I find that’s really interesting about this, Steve and Dave, the founders, initially failed and became successful when they changed how they looked at the business and said, “This is an entertainment business. This is not a golf business.” Then Callaway buys it thinking it’s a golf business, not an entertainment business, and the whole thing spun apart. And I sit there and I go, “This is where understanding a history of a company is so important.” If Callaway had asked Dave and Steve one question, “What was the insight you had that made this successful?” And they said, “This is not about golf, this is about entertainment.” And if they’d listened to it, the outcome of this would have been really different. The DNA of the business was that lesson that was then ignored by the acquirers. Dave Young: It was Margarita’s and hot wings. Stephen Semple: Yes. Dave Young: It was hanging with your friends. It was a different version of a pool hall. It was a different version of a bowling alley or- Stephen Semple: It’s a different version of David Busters. Dave Young: Yeah. Stephen Semple: Yes. It’s a different version of all those things. That is the environment in which they were operating. Dave Young: And I think if you take it just even at a slightly deeper level, it’s an extroverted golf experience. Stephen Semple: Yes. Yes. Dave Young: There are many, many golfers who golf to get away from everyone. Stephen Semple: Yes. Dave Young: Right? There are some that golf to be with their buddies or for business purposes or whatever. But I know so many people that will golf by themselves early in the morning. You couldn’t drag them into a Topgolf because just loud rock and roll and- Stephen Semple: Here’s where you could drag them into a Topgolf if you said, “Hey, let’s do a corporate retreat. Let’s do a team building thing. Let’s do something.” Because your choice is this. You can go to a restaurant, you can go to a bar or you can go to this thing where you do something together as a group of people. And frankly, that ended up being a lot of the Topgolf business was that exactly. Dave Young: Oh, sure. It might’ve been Julie’s work. I don’t think it was Wizard Academy. We thought about doing a Christmas party at Topgolf and then we just decided that’s too much money on stuff that none of us really like anyway. But it’s an outing. Stephen Semple: Right. But it’s an outing and it’s say you had an office where half the people were golfers and half weren’t. It was a way better outing than going to a golf course because everyone would now have fun. Dave Young: Yeah. Stephen Semple: So here’s the lesson. I want to leave our listeners with a lesson here because here’s what I think it is. If your business is struggling a little bit, sometimes you have to really understand what is your business. Jewelers who sell engagement rings are not in the diamond business. They’re in the connection, love, commitment business. If you are in the marriage business, you’re in the commitment business, you’re in the love business, you’re not in the sparkly diamond business. And to me, this was the most interesting example of looking at it going… And one could say, “Well, sports entertainment, kind of the same.” No, they aren’t. They live in different places in the human mind and are thought about differently. And when you’re considering doing something are in completely different worlds and you need to understand the business you’re in. Because that was the only thing that changed. The only thing that changed was that discovery that made them successful and the loss of that discovery that blew them apart was that one thing. Dave Young: And I think they’re still doing well, right? Stephen Semple: Oh, they’re actually doing well because the private equity company that bought them, guess what they knew? Dave Young: Entertainment. Stephen Semple: Like bought it from Callaway. What freaking business they were in. Dave Young: The two guys that invented it, right? A casino group would have been a better purchaser than Callaway. Stephen Semple: Well, yes. And if you actually take a look at- Dave Young: Callaway never should have bought it because they didn’t- Stephen Semple: Callaway should never bought it. And the company that bought it actually has a lot of entertainment businesses. So it’s going to do great. Dave Young: So the two guys that founded it, how much that Callaway money they walk away with? Stephen Semple: I was never able to figure out how much of the Callaway money that they walked away with other than they did very, very well and went on to invest in some other businesses. Dave Young: They’re doing fine. They’re doing fine. Stephen Semple: They’re doing fine. Dave Young: They’re living a good life. Stephen Semple: Rumors have it because I wasn’t able to find exact numbers, but rumor has it that… Because remember, they got two payouts. Payout number one was a private equity company bought them. I was never able to find out for how much. They still had ownership in the Callaway. Rumor has it that they walked away with a billion, something like that. Dave Young: I’d split a billion with you, Stephen. Stephen Semple: Yeah, there you go. That there’s some walking around cash. Yep. Dave Young: All right. I got an idea for us. All right. Are you in? Just say if you’re in, because this is a new business idea. Stephen Semple: Okay. There we go. Dave Young: Top bowling. You throw a bowling ball as far as you can and try to hit a target. Stephen Semple: Yeah. You know what? The targets would have to be really close. Dave Young: Yeah. There’s margaritas, there’s hot wings, burritos. Stephen Semple: Yeah. Alcohol and throwing bowling balls. I don’t know if should go together. Dave Young: I’m just saying. Live targets. Wait, that’s not a good idea. Well, next time you’re in Austin, maybe you and I should pop on over to Topgolf and have some hot wings. Stephen Semple: Done. Let’s do it. Dave Young: All right. Stephen Semple: Let’s do it. Awesome. Dave Young: Thanks for telling the story at Topgolf. I look forward to hearing what I have to say about Nebraska Furniture Mark at some point. Stephen Semple: All right. Awesome. Dave Young: Thanks, Stephen. Stephen Semple: Thanks, David. Dave Young: Thanks for listening to the podcast. Please share us. Subscribe on your favorite podcast app and leave us a big, fat, juicy five-star rating and review at Apple Podcasts. And if you’d like to schedule your own 90-minute Empire Building session, you can do it at empirebuildingprogram.com.

All the fun and insight of GameFace in 1/3 of the time! Assassin's Creed Black Flag Resynced, Mass Effect-like The Expanse, the new Steam controller, a pick-your-own-games Game Pass plan, Nintendo is sued over Trump's tariffs, Tomodachi Life, Vampire Crawlers, and much more!

Join Alison and TL;DR as we look at feminism, the only answer to the chaos of our times!

GameFace in 1/3 of the time with improved production values! It's GameFace TLDR! We review Pragmata, Mouse: P.I. for Hire, and Replaced! Plus, Game Pass loses Call of Duty and gets a price decrease, new details on The Last of Us Part III and the next God of War, Metro 2039, FromSoftware's upcoming films, and much more!

This episode begins with reactions to Justin Bieber's weekend 2 Coachella performance. TLDR: we are not well. Then, an update on the Alix Earle/Alex Cooper situation, and an honorable mention to the Kim/Lewis soft launch and Dylan Sprouse. Links:https://x.com/judrewstin/status/2044625616608088284?s=42https://www.instagram.com/p/DXVI72_AXKc/?igsh=MTFrODVjY2ZiOWN0dQ==https://www.tiktok.com/t/ZP8gCR7BM/https://www.tiktok.com/t/ZP8gCJdoe/https://www.tiktok.com/t/ZP8gCNsyX/https://www.instagram.com/p/DXVLCx1CeM5/?igsh=MWswYXBtamQxY255YQ==ShopMy: https://shopmy.us/shop/commentsbycelebsCodes: SKIMS.com - after you place your order, be sure to let them know we sent you! Select "podcast" in the survey and be sure to select our show in the dropdown menu that followsShopify - Sign up for your one-dollar-per-month trial today at Shopify.com/commentsWatch the new season of Vanderpump Villa on Hulu and Hulu on Disney+ for bundle subscribersLearn more at Starbucks.com/partnersSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Join Alison and TL;DR as we look at feminism, the only answer to the chaos of our times!

Listen If You Want to…. Couchella vs Coachella A catchy song that turned out to be AI BS Our questions about Artemis Why Testaments is a must watch And a TLDR need to know. See omnystudio.com/listener for privacy information.

Listen If You Want to…. Couchella vs Coachella A catchy song that turned out to be AI BS Our questions about Artemis Why Testaments is a must watch And a TLDR need to know. See omnystudio.com/listener for privacy information.

Listen If You Want to…. Couchella vs Coachella A catchy song that turned out to be AI BS Our questions about Artemis Why Testaments is a must watch And a TLDR need to know. See omnystudio.com/listener for privacy information.

GameFace in 1/3 of the time with vastly improved production values! We analyze the big changes coming to Xbox! The latest on GTA VI, Spider-Man 3 leaks, a Forza Horizon 6 preview, reviews of Samson and Pokemon Champions, Double Fine's pottery MOBA Kiln, and more!

Listen If You Want to…. Couchella vs Coachella A catchy song that turned out to be AI BS Our questions about Artemis Why Testaments is a must watch And a TLDR need to know. See omnystudio.com/listener for privacy information.

In Episode 153, hosts Alyce and Laura are* going to the MAUL later, thank you for asking. Laura whips out the good sh-t for Happy Hour We made a Sally Ride error and every woman in STEM that left an indelible mark on the world rolled over in their grave… including Sally Ride, probably.  The Mandalorian and Grogu is coming out in May, a few weeks after May the Fourth. You'd think that would be a good marketing tie in, yeah? Nope, Star Wars Day is all about Return of the Jedi this year for some reason. Maul about Maul! He's everywhere! In the news! On Disney+! Probably other places! Maul - Shadow Lord is getting a second season. Plus, Filoni talks Maul with Esquire. Protip: there's a great TLDR on SWNN. Jon Favreau will be at CinemaCon to get everyone hyped about the upcoming Mando movie (via Deadline) Grogu is the real star of this filmmaker roundtable Ben There, Done That! (patting ourselves on the back for that one) Stephen Soderbergh reacts to Lucasfilm's pass on his Ben Solo-centric film (BKMAG via Nerdist) In book news, Star Wars' new horror offering and, on the opposite end, an adult coloring book! Multitudes, Star Wars contains, hmmm? LEGO Smart Bricks continue to mystify the masses. Now Mark Hamill weighs in. We're back with Recap on Tap! Maul - Shadow Lord is officially here and we're diving into episodes 1 and 2 with a SPOILER discussion on all the old and new faces, cute lil guys and insane bad-@ssery abound!  Feeling a little lost after the first episodes of Maul - Shadow Lord? We recommend Youtini's Everything You Need to Know article.  BOOSHKEEDOO! Twitter: @forcetoastpod | @sLeiaAllDay | @ShutUp_Laura Instagram: @forcetoastpod Bluesky: forcetoastpod.bsky.social Email: forcetoastpod@gmail.com Website: forcetoastpod.com *This podcast contains a sh!t ton of profanity and boozin. You can find a bleeped version of this podcast absolutely nowhere. Cheers!

GameFace in 1/3 of the time with superior editing, B Roll, and production! Pop culture reviews and the Super Mario Galaxy Movie, new details on the PS6 handheld, Bungie after Marathon's poor sales, reviews of Super Meat Boy 3D and Legacy of Kain: Ascendance, the State of Decay 3 debut was fake, Nintendo lowers the price of digital games, the Mass Effect TV show is being written for non-gamers, and so much more!

Here is the TLDR on Ag news for the first week of April!Enjoy!Produced by Atlas AG Media Solutions:https://www.atlasmediagroup.us

GameFace in 1/3 of the time! We analyze the massive PlayStation hardware price increases and what they mean for PS6! Plus, Ocarina of Time and Star Fox coming to Switch 2, our Life is Strange: Reunion review, the Xbox Partner Preview for March 2026, Tomodachi Life: Living the Dream, and more!

Countries must be governed.Order must be established.People must be protected."For there is no authority except from God, and those that exist have been instituted by God." (Rom. 13)Politics is not evil - at its core, just a tool for ordering society - but the pursuit of power for its own sake often is.

GameFace in 1/3 of the time! We review the polarizing (and gigantic) Crimson Desert! Plus, Epic lays off 1000 Fortnite employees, the kids don't care about Final Fantasy, the DLSS 5 saga updates, The Eternal Life of Goldman, scary February game sales, The Last of Us HBO Season 3 casting, the Subnautica 2 drama hits insanity levels, and more!

Episode 343 hosts Rick O'Neill (Digital Marketing Expert from Brighton, UK) We discuss the major shifts in aesthetic marketing over the past 12–24 months - driven by AI technology and the changing behavior of patients. We explore: How SEO and website optimisation are more important due to how tools like ChatGPT search the web. How patients can take up to 12 months with numerous touchpoints before converting to a booking The requirement for an 'always-on, multi-channel' ecosystem beyond Instagram. Tool likes 'AnswerThePublic' to understand what patients are Googling The benefit of writing experience-based blog articles on your website TLDR summaries for AI visibility Email marketing segmentation Cautious AI tool use inclusing chatbots and note-takers LTF's new 'Black Belt Digital' platform - offering AI-built websites, education, templates and resources IA Community - Inside Aesthetic's new upcoming app 00:00 Introduction 00:42 Special Guest: Rick O'Neil - Returns and AI Shift 01:37 MCAS Recap and Business Focus 02:46 Rick Background and LTF Story 07:10 Big Digital Changes Explained 11:54 Zero Moment of Truth 18:08 Build a Content Ecosystem 18:44 Answer the Public Workflow 20:37 Write Blogs That Rank 22:32 Repurpose and TLDR for AI 26:58 Client Adoption and Practical Tips 29:49 Budgeting and Marketing Mindset 33:56 Content Creation vs Distribution 38:07 SEO Basics and Technical Fixes 42:56 SEO Intent Signals 44:28 AI Tools for Clinics 48:04 Websites in 2026 51:12 Social Media That Converts 56:50 Search Meets Instagram 58:20 Black Belt Digital Platform 01:07:52 Listener Questions 01:12:08 Future Patient Journey 01:15:36 Where to Start Now 01:18:17 Final Wrap and Plugs Black Belt Digital - Discover Black Belt Digital: LTF's new platform dedicated to helping clinics with their aesthetic digital business marketing success. Get expert marketing knowledge, free resources and a professional website builder. JOIN THE WAITING LIST FOR IA COMMUNITY (OUR NEW APP) ALL IA LINKS & CONTACT INFORMATION

Nerdy Bitz: TL;DR – The Science of LonelinessThe Reverend Tracy debuts a brand-new Nerdy Bitz segment, TL;DR—where big ideas get broken down into powerful, bite-sized insights you can actually use (because who has time to read everything, right?).In this inaugural episode, Tracy dives into the groundbreaking research of social neuroscientist John Cacioppo, connecting it directly to the themes explored in Friends Talking Nerdy Episode 453: Talking About History: Decline of Multigenerational Homes.Drawing from Cacioppo's influential book Loneliness: Human Nature and the Need for Social Connection, this Nerdy Bitz unpacks a fascinating—and honestly a little unsettling—truth: loneliness doesn't just feel bad emotionally… your brain treats it like a physical threat.We're talking survival-mode.The Reverend Tracy explores how humans are biologically wired for connection, and how the absence of social bonds can trigger the same stress responses as danger—raising anxiety, impacting health, and reshaping behavior in ways we often don't even realize. When you zoom out, this insight adds a whole new layer to the decline of multigenerational living in America. It's not just a cultural or economic shift—it may actually be rewiring how we experience community, safety, and belonging.This TL;DR episode bridges science and society, showing how something as simple as who we live with can have deep neurological consequences. It's quick, it's thought-provoking, and it might just change how you think about being “alone.”New episodes of Friends Talking Nerdy drop every Monday wherever you listen to podcasts.As always, we wish to thank Christopher Lazarek for his wonderful theme song. Head to his ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for information on how to purchase his EP, Here's To You, which is available on all digital platforms.Head to Friends Talking Nerdy's⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠for more information on where to find us online.

I'm part of the last generation to remember life before social media. My first brush with it was Myspace, late in high school. Facebook launched in 2004, but it didn't really take off until I was in college. That means my generation didn't grow up with it — we grew up into it.We had to incorporate this new, magical tool just as we were figuring out adulthood, relationships, politics, and what we believed about the world. We were digital toddlers trying to run with digital fire.

All the fun and information from GameFace in 1/3 of the time! We tackle the massive controversy over NVIDIA's DLSS 5! Plus, reviews of Monster Hunter Stories 3, John Carpenter's Toxic Commando, and Slay the Spire 2! More Xbox Helix details from GDC 2026, and more!

All the info and fun of GameFace in 1/3 of the time with vastly improved editing and production values! Our Resident Evil Requiem review! Plus, Pokemon Winds and Waves, Marathon's Server Slam, PlayStation stops releasing its games for PC, Monster Hunter Stories 3, the next God of War, the next Guitar Hero, and more!

Last night, President Donald Trump strongly defended his tariffs in his State of the Union address. He made the case for future tariffs, despite the Supreme Court decision last week striking down the centerpiece of his tariff policy. Trump also expressed hope that import taxes will someday replace income taxes. Plus, Nvidia is looking to get back into the consumer market, and mortgage rates dipped below 6% this week.

Last night, President Donald Trump strongly defended his tariffs in his State of the Union address. He made the case for future tariffs, despite the Supreme Court decision last week striking down the centerpiece of his tariff policy. Trump also expressed hope that import taxes will someday replace income taxes. Plus, Nvidia is looking to get back into the consumer market, and mortgage rates dipped below 6% this week.

In “The Cost of Truth,” Vince Fakhoury Horn speaks with Daniel Klein—a former religious Zionist settler turned outspoken critic of the ideology—about dehumanization, self-forgiveness, and the courage required to speak truth at the risk of losing everything (except one's humanity).