Podcasts about why chatgpt

SEO is changing faster than ever, and what worked even a year ago might already be outdated. In this episode, I'm joined once again by SEO expert Kristie Plantinga to unpack what therapists actually need to know about ranking online in 2026. We dive into the rise of AI search, whether AI optimization (AIO) is really different from SEO, and why therapists may have more of an advantage than they realize as tools like ChatGPT and Google's AI Overviews continue to shape how potential clients find providers. We also talk about what strategies are still worth your time, what advice has become outdated, and why Google Business Profiles may now be one of the highest leverage marketing tools available. Kristie shares her thoughts on blogging in the age of AI, how to position yourself as the go to expert in your niche, and why being known for one specific thing matters more than ever. If you've been wondering where to focus your marketing efforts without wasting time on tactics that no longer work, this conversation will give you clarity and direction.   Topics Covered in this Episode: 4:48 - Why ChatGPT is recommending private practices over big tech companies 10:45 - The biggest SEO priorities therapists should focus on right now 14:20 - Why your Google Business Profile may matter more than your website 16:55 - What Google actually rewards in an AI driven world 18:10 - Is blogging still worth your time in 2026? 21:00 - The search terms and specialties therapists should be targeting 23:00 - How becoming known for one thing can transform your visibility   Resources Mentioned: Connect with Kristie: placedigital.com  Find out more about Alma here: helloalma.com/danielle Take 50% off your first 3 months of Simple Practice + a 7 day free trial using the link: simplepractice.com/danielle   Fill Up Therapists: $0-$60k   If you are needing more private pay clients in your practice in 2026, the Practice Accelerator is the perfect fit for you. Use the code ALLIN as a podcast listener to get $100 off at checkout.    Scale Up Therapists: $60-$200k+ Group practice owners, content creators and therapists scaling beyond 1-1.  Apply here for the next round of Scale Up Mastermind where I help therapists create additional revenue streams and scale to multi six and seven figures. 

Artificial intelligence has quickly become part of everyday life. It helps people write emails, summarize meetings, generate code, and answer questions in seconds.  But the next phase of AI may be less about generating information and more about taking action.  In this episode, Ty Panagoplos, Executive Vice President and Chief Technology Officer at Scotiabank, explains how AI has evolved from a specialized technology used by experts into a mainstream tool available to almost anyone. He explores one of the biggest trends shaping the industry today: Agentic AI.  Ty breaks down what Agentic AI actually is, how it differs from generative AI, and why organizations are paying close attention to its potential to automate increasingly complex tasks. He also discusses how banks are approaching AI adoption, balancing innovation with security, governance, and trust.  He also discusses:  Why ChatGPT changed the public conversation around AI and accelerated adoption  The difference between Generative AI, AI agents, and fully autonomous Agentic AI  How AI is already improving productivity across organizations  Why Scotiabank joined an AI consortium focused on safe AI deployment  The guardrails banks need before autonomous AI can be widely adopted  The risks posed by misinformation, hallucinations, security threats, and bad actors  The emerging role of AI agents in customer service and software development  How AI could help create more personalized banking experiences in the future  What the next three to five years of AI innovation may look like    For legal disclosures, please visit http://bit.ly/socialdisclaim and www.gbm.scotiabank.com/disclosures  Key moments this episode:  00:01:45 Ty Panagoplos' background in technology and banking  00:02:50 Why AI suddenly became mainstream  00:04:45 The biggest opportunities AI creates for businesses  00:06:05 How AI is already being used across banking  00:07:55 What Scotia Intelligence is and how it works  00:09:55 Generative AI versus agentic AI explained  00:12:00 Why Scotiabank joined an AI consortium  00:15:05 The risks of AI and the importance of guardrails  00:16:50 Privacy, hallucinations, scams and other concerns  00:18:00 The next wave of AI tools  00:19:30 Frontier models and the future of cybersecurity  00:20:55 What AI could look like over the next three to five years

Position Your Nonfiction Book for Bestseller Success with Wiebke TaschAnika sat down with Wiebke Tasch to explore a hard truth in the publishing world: most nonfiction books fail not because the writing is bad, but because the positioning was wrong before the first word was ever written. Wiebke shares her journey from feeling stuck in a German PR agency to accidentally writing an Amazon bestseller about heartbreak, and how that success led her to found Digital Authors. The conversation reveals why a book is not just a vanity project, how data should dictate your book's direction, and why the "AI hype train" might be the worst thing to happen to your legacy.In This EpisodeHow a personal crisis and a lack of available therapy led to Wiebke's first bestselling bookThe difference between a vanity project and a strategic credibility builderWhy "guessing" your book's niche is a recipe for failure—and how to use data insteadThe cultural divide: Why European authors feel they must be an authority first, while US authors use a book to become oneRepositioning a business coach: How pivoting from "digital nomad" to "building a business" resulted in 9x more pre-salesThe "personal story formula": Why frameworks alone are boring and how to keep readers engagedThe role of AI in publishing: Why ChatGPT makes for terrible books but a great proofreaderA surprising market trend: Why the flood of AI books has created a unique opportunity for genuine human authors right nowThe psychological mirror of writing: How becoming an author forces you to confront your fear of visibilityThe "5-Day Amazon Window" and the secret to getting the algorithm to market your book for youCrafting a 3-month launch strategy and why Sunday afternoon is the worst time to hit "publish"Timestamps00:00 Introduction: Why do most nonfiction books fail to find an audience?01:24 From Berlin PR to digital nomad: The origin of Digital Authors 03:10 Writing Why Can't You Love Me? and accidentally hitting a bestseller niche 05:38 Learning from failure: Why the second and third books flopped 07:27 Market analysis: Using data to find demand before you outline the book 11:38 Europe vs. US publishing culture: "Who are you to write?" vs. "Write to become." 14:27 Case Study: Repositioning a "digital nomad" book to 9x the pre-sales 17:34 The writing process: Blending personal storytelling with actionable frameworks 23:28 The truth about AI in publishing: Use it to proofread, not to write your legacy 27:55 The personality development of writing: Overcoming the fear of being seen 32:01 The 5-Day Amazon Window: How to trigger the algorithm to push your book 36:31 A 3-month book launch strategy: Pre-sales, webinars, and launch parties 39:50 Positioning differences between established experts and brand-new authors 45:54 A special 25% discount offer for authors under 40 49:33 Favorite mantra: Trusting your own intuition above all elseKey Insights & TakeawaysInsight 1: Positioning Precedes Writing Most authors write what they want to say without checking if anyone is actually searching for it. By running a data-driven market analysis first, you can identify high-demand, low-competition niches, ensuring your book has a built-in audience before you even outline the first chapter.Insight 2: Frameworks Are Boring; Stories Stick If your book is just a step-by-step guide, it should have been a PDF. To keep readers engaged in an era of scrolling and short attention spans, authors must blend their conceptual frameworks with deeply personal stories and actionable exercises. Readers need to see themselves in your journey.Insight 3: The 5-Day Amazon Algorithm Window Publishing your book quietly and starting marketing two months later is a death sentence for sales. Amazon gives new releases a 5-day window. If you can drive massive traffic and conversions during those first five days, the algorithm will pick up the book and essentially take over the marketing for you long-term.Insight 4: AI is for Polishing, Not Creating While the market was briefly flooded with ChatGPT-authored books, readers are rejecting the generic, soulless writing. A book is a timeless authority tool meant to last for decades. Use AI for final grammar checks or structuring thoughts, but the core ideas and the voice must remain deeply human.Insight 5: Writing a Book is an Act of Personal Transformation Writing a book forces authors—especially women—to confront their imposter syndrome and fear of visibility. The process of claiming your expertise on the page ultimately translates to how you show up in your business, handle speaking engagements, and price your services.Resources & Links MentionedDigital Authors (Wiebke's Agency)About Wiebke TaschWiebke Tasch is the founder of Digital Authors and a publishing strategist who helps entrepreneurs and thought leaders turn their expertise into books that build authority and attract opportunity. Originally from Germany with a background in journalism and PR, Wiebke walked the hard path of publishing—first writing an accidental bestseller, followed by books that flopped. These experiences taught her the crucial importance of data-driven market positioning. Today, she helps authors identify demand, outline their frameworks, and execute high-impact book launches that leverage the Amazon algorithm.Connect with WiebkeLinkedIn: https://www.linkedin.com/in/wiebke-tasch-%F0%9F%93%9A-43731b164/?locale=enDigital Authors Website: www.digital-authors.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

To kick off our FY27 tax-time series, Dave and Hayden are joined by Navarre Trousselot, founder of Navexa — an investment tracking and tax reporting platform (if you've heard of Sharesight, it plays in the same space). Because here's the thing: most of us agonise over what to buy, then put zero thought into how we sell — and selling is the part where you actually get your money back. As Navarre puts it, too many investors are "selling in the dark" and handing the ATO far more than they need to.In this episode we'll discuss:

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

What if the reason you're not getting more sales isn't your website... it's that ChatGPT doesn't know you exist?Millions of people now ask ChatGPT, Claude, Gemini, and Perplexity which products, services, and businesses they should choose. If your business isn't being recommended, you're invisible at the exact moment customers are ready to buy.In this episode, PR expert and former U.S. diplomat Gloria Chou shares the five biggest ways to increase your AI visibility—without spending thousands on SEO agencies, paid ads, or complicated software.You'll learn: Why ChatGPT recommends some businesses but not others  The difference between traditional SEO and AI visibility  Why PR is becoming more important than ever for getting discovered  How media mentions influence AI recommendations  Website changes that help AI better understand your business  Free ways to check whether AI is already recommending you—or your competitors  Simple steps you can take today to become more visible in AI search If you're a small business owner, Shopify brand, Etsy seller, coach, consultant, or entrepreneur, this episode will help you understand how customers are finding businesses in 2026—and what you need to do to become the one AI recommends.In my free AI Visibility Masterclass, I'll show you how small business owners are using PR, media features, and credibility signals to become the businesses AI recommends. You'll learn how to earn press without hiring a PR agency, increase your AI visibility, and get discovered by customers at the exact moment they're ready to buy.Watch the free masterclass:

How do you know whether an AI agent is doing its job or quietly failing in production?Galileo is building the trust layer for AI. Its evaluation and observability platform is how enterprises measure whether the output of an LLM or an agent is good or bad.Galileo started before "LLM" was even a word. When Atin showed his prototype to Stanford's Chris Ré, his own first question was "what is a language model?" Today its customers include Reddit, Airbnb, P&G, Comcast, and six of the Fortune 50. Atin spent a decade in big tech before co-founding Galileo with Vikram Chatterji in early 2021. He worked on the knowledge graphs behind Siri at Apple, then became one of the leads and architects of Michelangelo, Uber's AI platform, that hosts thousands of models across pricing, ETA, and demand.That Uber experience taught him the lesson the whole company is built on; that in AI, observability and evaluation are the real bottleneck, and bad data is catastrophic.As ChatGPT turned every AI output into something a user sees directly, the measurement problem went from academic to mission-critical. So Atin made a contrarian bet: instead of using giant LLMs to judge other LLMs, Galileo built Luna, small 1-3B parameter models that run evals at breakthrough latencies of 100 milliseconds and below.If you are excited about how AI actually gets shipped, trusted, and controlled inside real enterprises, this episode is for you.00:00 - Trailer01:14 - From India to Apple, Uber, and Galileo01:34 - Where the name "Galileo" came from02:38 - Building Siri's early knowledge graphs at Apple03:29 - Becoming an architect of Uber's Michelangelo05:15 - Why every AI output is now mission-critical06:45 - How Atin and Vikram zeroed in on Galileo07:42 - "What is a language model?"09:38 - Building the world's first feature store at Uber11:27 - Language models and tokens, explained simply14:19 - Where the observability insight came from15:53 - Quantifying uncertainty and hallucinations16:36 - The first customers and first use case19:15 - How the product evolved from a data scientist tool23:18 - Why ChatGPT changed everything for Galileo23:57 - The enterprise AI adoption curve, 2021 to 202626:35 - Why they built the Luna model28:32 - Turning LLM "writers" into "calculators"28:51 - Attacking the latency problem31:48 - Luna: the modeling and infrastructure innovation33:09 - What evals are, and why they blew up34:26 - The case for small language models36:58 - What "general reasoning" really means40:39 - AI usage is exploding — and why that matters43:08 - Online vs offline: the "it worked on my machine" problem44:33 - The evals flywheel and evals-driven development46:56 - Galileo in a nutshell47:39 - What real agents in production look like today49:30 - A sales intelligence platform, powered by Galileo50:47 - The agent control product52:12 - Building GTM as a hardcore engineer from India54:43 - Garbage in, garbage out: nailing the ICP55:42 - How the pitch changed from customer 1 to 2057:27 - Why Atin switched from CTO to CPO-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

This Capricorn Full Moon (June 29, 2026) asks you to hold two opposite truths at once: expansion in one hand, restraint in the other, yes and not yet, both true at the same time. In this Moon Musings episode we go beyond the horoscope into the decan, the mythology, the asteroids, medical astrology and herbal medicine, and what shifts the moment Jupiter crosses into Leo just hours later. Stay to the end for an Akashic Oracle reading for all 12 signs (tune in to your Sun and Rising).

In this Karma School of Business crossover episode, Sean Mooney is joined by Lloyd Metz, Managing Partner of ICV Partners; Doug McCormick, Managing Partner of Oridian Capital Partners; James Aylward, Chief Product and Technology Officer at BluWave; and Nathan Plummer, Co-Executive Director of Venture Café Global Institute. The group explains how AI moved from long-running concept to practical business tool, and what that means for private equity firms and portfolio company leaders right now. They cover agentic AI, data readiness, applied AI use cases, internal tooling, security concerns, and the leadership challenge of getting teams to adopt new ways of working. This is a practical conversation for business builders who want to move past AI hype and start creating real value—hit play. Episode Highlights 2:13 - James Aylward's path from Fidelity's AI incubator to BluWave's technology strategy 5:08 - Nathan Plummer on Venture Café's role in connecting startups, investors, and innovators 9:17 - Why ChatGPT marked a "Gutenberg moment" after decades of slower AI progress 13:10 - AI shifts from autocomplete and thought partnership to writing major chunks of code 18:06 - Applied AI moves beyond efficiency into biotech, robotics, imaging, and new product creation 23:28 - Why proprietary data may become the real moat for private equity-backed companies 35:42 - AI literacy, adoption, and the leadership work required to make tools stick 44:05 - BluWave's internal AI tool shows how company data can power faster decisions For information on Oridian Capital Partners, go to https://oridiancapital.com/ For information on ICV Partners, go to https://www.icvpartners.com For information on BluWave, go to https://www.bluwave.net

Does online therapy actually work? Or is it just a pandemic hangover? Therapist Stephanie McAllister joins me to talk about virtual counselling, finding the right fit, crying on camera, car sessions, and why ChatGPT isn't a substitute for human support. In this episode we talk about: • Online therapy vs in-person counselling • Whether virtual counselling actually works • Doing therapy from your car (yes, really) • Why finding the right therapist matters • One bad experience doesn't mean therapy isn't for you • Small-town privacy and avoiding awkward supermarket encounters • Why tears aren't something to apologise for • The pros and cons of online counselling • Why ChatGPT isn't your therapist • Accessing specialist support from anywhere Find out more about Stephanie here: https://virtualconnect.ca/  Find out more about Suzanne here: https://www.suzanneculberg.com/  Want to be a guest on The Nope Coach? Send Suzanne Culberg a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/thenopecoach 

Most nutritionists are using AI. Very few are using it in a way that actually saves time!   In this episode, Stephanie breaks down five things that make the biggest difference when it comes to using AI to save time in your nutrition business, including why the platform you're using might be quietly working against you, and how to set things up so AI sounds like you from the first draft instead of the fifth.   This episode is also a preview of Stephanie's upcoming live AI Workshop, where she walks through the full implementation together. Details and registration link below!   In this episode, you'll learn: Why ChatGPT and Claude produce very different results and which one is actually better for nutrition businesses What a brand book is and why setting one up cuts your editing time dramatically How to turn one piece of long-form content into a full week of marketing material without starting from scratch What Claude Skills are and how they keep you from re-explaining your business every single time you sit down to create Quick AI wins you can start using this week for client communications, content planning, and repurposing old content Links and resources mentioned:   Register Here: AI For Nutritionists Workshop on June 16, 2026   Get fully booked and make consistent income inside Booked Out Nutritionist   Save $100 on Launch Your Nutrition Biz with code PODCAST in the checkout   Watch Stephanie's free workshop 6 Steps to Start Your Nutrition Business & Sign Your First Paying Clients  

If ChatGPT is still the only AI tool you're using, this episode will help you take the next step. In this episode of The Online Course Show, I'm sharing the AI tools I'm actually using in 2026. Not a giant list of random apps. Just the ones that are saving me time and changing how I work. I talk about why I've been using Claude as my daily driver AI, how to get better results with simple prompting habits, why “do research” is one of my favorite phrases to add to a prompt, and how Wispr Flow has made it much easier to give AI the context it needs. I also walk through Claude Cowork, how I use Rocky in my own business, why Pikzels has become part of my YouTube workflow, and why Claude Code is worth trying even if you don't think of yourself as a coder. If AI feels overwhelming, don't try to learn everything at once. Pick one tool. Try one workflow. Build from there. IN THIS EPISODE • Why ChatGPT is only the beginning • Why I'm using Claude as my main AI tool • How to use “do research” to get better answers • Why AI can sound confident and still be wrong • How to use ELI5 prompts • How to make AI review your work from your audience's perspective • Why Wispr Flow makes prompting faster • What Claude Cowork does • How Pikzels helps with YouTube thumbnails • Why Claude Code is useful beyond coding CHAPTERS 00:00 Why ChatGPT is only the beginning 01:57 Claude as my daily driver AI 05:42 Prompting tips that make AI more useful 08:54 A simple demo of AI guessing confidently 11:15 ELI5 and audience-review prompts 15:45 Wispr Flow and voice-first prompting 19:24 Claude Cowork and AI agents 22:36 Pikzels for YouTube thumbnails 25:30 Claude Code for apps and bigger projects 29:15 Where to start if AI feels overwhelming LINKS AND RESOURCES Claude: https://claude.com/download Wispr Flow: https://wisprflow.ai/r?JACQUES92 Pikzels: https://pikzels.com/?via=jacques63 Watch on YouTube: https://youtu.be/AzQiZEiQF-8 Get your Personalized AI Game Plan: https://jacqueshopkins.com/ai-start Some links may be affiliate links, which means I may earn a commission at no extra cost to you.

Are you using ChatGPT, AI, or a generic meal plan to figure out what your gymnast should be eating?AI can be helpful for a lot of things, but when it comes to your gymnast's nutrition, growth, development, injuries, training load, and recovery, there are major limitations parents need to understand.In this episode, Christina kicks off a four-part series on what ChatGPT and AI do not know about your gymnast's nutrition.Because while AI may sound confident, polished, and helpful, it cannot properly assess your gymnast's growth chart, puberty progression, injury history, under-fueling patterns, or what they actually need to grow, recover, and adapt to training.And when a gymnast is already under-fueled, a generic AI-generated meal plan can give parents a false sense of security while missing the deeper issue.In this episode, Christina breaks down why growth is one of the biggest clinical indicators of whether a gymnast is getting enough nutrition, why height and weight cannot be looked at in isolation, and why automated nutrition advice often falls short for pediatric and adolescent athletes.Because your gymnast does not just need enough fuel to get through practice.They need enough fuel to grow, develop, repair, recover, stay healthy, and actually get stronger from the work they are putting in.In this episode, we cover:❗ Why AI cannot replace individualized support from a qualified healthcare provider❗ Why ChatGPT-generated meal plans can be risky for gymnasts❗ Why your gymnast's growth chart matters more than one height or weight measurement❗ Why “not losing weight” does not mean your gymnast is properly fueled❗ How under-fueling can show up as slowed growth, stalled development, injury, fatigue, and poor recovery❗ Why generic calorie calculators often miss what gymnasts actually need❗ Why pediatric and adolescent nutrition is different from adult sports nutrition❗ How AI can create a false sense of security for parents❗ Why labs, meal plans, and nutrition recommendations need to be individualized❗ Why the right support looks at your gymnast's full history, not just what they eat in a dayAI may have its place, but it cannot understand your gymnast the way an experienced pediatric and adolescent sports dietitian can.Your gymnast is not a generic athlete.They are a growing, developing child or teen with unique needs, training demands, injury history, preferences, challenges, and goals. And their nutrition needs to reflect that.Links & ResourcesThe Balanced Gymnast® Program (Level 5–10)Connect with Christina on Instagram @the.gymnast.nutritionist christinaandersonrdn.com

People are already using AI during divorce whether attorneys like it or not.They're asking ChatGPT to:analyze texts from their exexplain legal documentshelp write co-parenting responsesbuild parenting schedulesorganize timelinesprepare for mediationand sometimes… emotionally spiral at 2amSo where's the line between using AI strategically and using it in a way that could quietly damage your case?Leave us a review!

Fresh out of the studio, Geoffrey Cain, author of Steve Jobs in Exile and Samsung Rising, returns to the Analyse Podcast to argue that the twelve years between Jobs's 1985 ouster and his 1997 return to Apple were not a footnote but the forge. Drawing on private archives at Carnegie Mellon and Stanford, unbroadcast footage from inside NeXT, and interviews with the people who lived it, Cain reframes the wilderness decade as the cause, not the gap, in Jobs's transformation. We trace the NeXT collapse and the failed IBM licensing deal, the parallel crucible of Pixar where Catmull and Lasseter barred Jobs from creative meetings, and the deep Japanese and Zen influences — Akio Morita, Sony, the beginner's mind — that Isaacson and Schlender underplayed. We close on Apple at fifty, John Ternus's ascent, and what Jobs would have done with AI. "The successes that we see in the world for every iPhone there is, for every SpaceX rocket there are perhaps dozens or maybe even hundreds of failures behind that we don't see. And so the wilderness, as they call it, this is the greatest moment in the lives of many founders. It's the wilderness that we all have to go through before we can achieve greatness, and if we don't go through that, then we don't learn those lessons." - Geoffrey Cain Profile: Geoffrey Cain, author of "Steve Jobs in Exile"LinkedIn: https://www.linkedin.com/in/gcain/Personal Site: https://geoffreycain.net/Episode Highlights: [00:00] Quote of the Day by Geoffrey Cain, author of Steve Jobs in Exile [00:30] What Geoffrey has been up after his first book: Samsung Rising [04:05] Working in the US House on technology policy & rebuilding America's industrial base [04:50] De-industrialisation, and rebuilding America's industrial base [05:24] The central thesis on Steve Job's exile [07:13] The Steve Jobs we don't know — before the turtleneck and the iPhone[09:07] The wilderness — where every great founder is forged[12:30] The failed coup against John Sculley[14:10] Was Jobs early or wrong about what universities needed?[16:31] Object-oriented programming — the real innovation Jobs couldn't see[18:36] Jobs of 1997 was not the Jobs of 1985[20:00] Technology does not change the world — it makes things easier[22:38] The butterfly effect — if NeXT had gone differently, no iPhone[25:13] A failure of ego — Jobs versus the company he hated[28:49] NeXTstep — twenty years into the future in 1990[32:24] Pixar as the parallel crucible — bought for $5 million[35:25] Toy Story and the IPO that made Jobs a billionaire[38:57] What the NeXT and Pixar years really reveal[40:38] Three biographies, three frames — Isaacson, Schlender, Cain[45:26] Why NeXT became the ugly duckling of Apple lore[48:12] The Japanese influence Isaacson never pulled on[51:30] Apple at fifty — Ternus and the era of execution over reinvention[54:11] How Jobs would integrate AI — quiet, in the background[55:10] The Apple-Google Gemini partnership and swallowed pride[56:38] Jobs as second mover — Macintosh, iPhone, the bicycle for the mind[57:30] Why ChatGPT and Claude would look ugly to Jobs[1:00:30] What NeXT veterans say about the Ternus appointment[01:02:33] What success means for the book[01:03:13] Closing Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.com

We talk a lot on this show about supplements, hormones, food, sleep, and protocols. But there is a layer underneath all of that we have not gone deep on until now. Our beliefs. Who we think we are. Why we are here. And the quiet, unconscious story running the show every single day. In this episode, Natalie sits down with her own therapist Mcayla Sarno, an EMDR specialist who has spent almost twenty years helping women get to the root of the beliefs that are silently shaping their bodies, their relationships, their purpose, and their health. Natalie also shares the very personal EMDR story that changed her life. The memory with her mom in the car. The belief she did not even know she was carrying. And what shifted when she finally looked at it through different eyes. This is a real conversation about why so many of us hit midlife feeling lost, why the empty nest hits harder than anyone warned us, what is actually happening when we try to control everything, and why a therapist asking the right question will always do something ChatGPT cannot. We talk about how core beliefs form, how they show up in the body, and the work it takes to finally come home to who we have always been underneath all of it. If you have been feeling like you have done all the outer work and something inside still is not clicking, this is the episode for you. WE GO DEEP ON •  What EMDR actually is and why it is not just for trauma •  The difference between understanding something and integrating it •  How core beliefs form and why they generalize to every area of our life •  The connection between unresolved beliefs and chronic health issues •  Why ChatGPT will never replace a real therapist asking the right question •  The empty nest grief nobody talks about and the mini mourning of every version of our kids •  Why control feels safe but never actually makes us safe (and what does) •  How external validation becomes the trap of our 30s and 40s and breaks open in our 50s •  The infant question that reframes everything: were you good enough the day you were born •  Natalie's personal EMDR story, the car memory with her mom, and the belief she was carrying •  Why 5-day intensives work when weekly therapy has not •  "I always was. I already am." The reframe every midlife woman needs Learn More About Mcayla Sarno Instagram ➜ https://www.instagram.com/drmcayla  Website ➜ http://drmcayla.com/    Thank you to our show sponsors:  MITOQ: Take control of healthy aging and longevity. Get 10% off using code NATALIEJILL at checkout on https://www.mitoq.com/  LEELA QUANTUM: Get 10% off your first Leela Quantum order with the code NATALIEJILL at checkout  at https://midlifeconversations.com/leela   SUNLIGHTEN: Sleep better. Recover faster. Stress less. Get Sunlighten infrared saunas HERE https://sunlighten.com  and use code NATALIEJILL to save up to $1,400!    Free Gifts for being a listener of Midlife Conversations! Mastering the Midlife Midsection Guide: https://theflatbellyguide.com/ Age Optimizing and Supplement Guide: https://ageoptimizer.com   Connect with me on social media! Instagram: www.Instagram.com/Nataliejllfit Facebook: www.Facebook.com/Nataliejillfit   For advertising inquiries: https://www.category3.ca/  Disclaimer: Information provided in the Midlife Conversations podcast is for informational purposes only. This information is NOT intended as a substitute for the advice provided by your physician or other healthcare professional. Do not use the information provided in this podcast for diagnosing or treating a health problem or disease, or prescribing medication or other treatment. Always speak with your physician or other healthcare professional before making any changes to your current regimen.  Information provided in this podcast and the use of any products or services related to this podcast does not create a client-patient relationship between you and the host of Midlife Conversations or you and any doctor or provider interviewed and featured on this show. Information and statements may have not been evaluated by the Food and Drug Administration and are not intended to diagnose, treat, cure, or prevent ANY disease. Advertising Disclosure: Some episodes of Midlife Conversations may be sponsored by products or services discussed during the show. The host may receive compensation for such advertisements or if you purchase products through affiliate links. Opinions expressed about products or services are those of the host and/or guests and do not necessarily reflect the views of any sponsor. Sponsorship does not imply endorsement of any product or service by healthcare professionals featured on this podcast.

What if AI was never meant to replace your intelligence… but expand it?   In this powerful episode of The Shiny Minds Show, I break down the BIG 3 AI tools shaping the future of business, coaching, creativity, and leadership in 2026: Gemini, Claude, and ChatGPT, and exactly how to use each one strategically instead of randomly.   Because successful people are no longer just “using AI.” They are orchestrating intelligence.   Inside this episode, I share: ✨ Why Gemini is the ultimate pattern recognition powerhouse ✨ Why Claude excels at deep strategy, documentation, and executive thinking ✨ Why ChatGPT is the execution engine that turns ideas into deliverables ✨ How to combine all three tools for maximum productivity and creativity ✨ The future of AI-powered coaching, leadership, and entrepreneurship ✨ Why your HUMAN intelligence still matters more than ever ✨ How to integrate AI without losing your soul, creativity, or identity   As an ICF Master Certified Coach, NLP Trainer, and founder of Neuro-Shine Technology™, I believe AI should never replace human wisdom. It should amplify it.   This episode is for: ✔ Coaches ✔ Entrepreneurs ✔ Leaders ✔ Creators ✔ Consultants ✔ Speakers ✔ Visionaries ✔ Anyone who wants to future-proof their career and business in the AI era   Because the future belongs to those who can blend:

Send us Fan MailRunning a business is hard enough…Now try doing it while your co-host is out of state, your kids are running wild, and you're juggling multiple businesses at once

AI is rapidly becoming part of how patients navigate complex illness—but most people are using it without understanding how it actually works, what it's designed to do, or where it can fail.In this episode of Long Covid, MD, Dr. Zeest Khan is joined by two experts:Dr Leeda Rashid (former FDA physician, digital health) Dr Jennifer Curtin (CEO of RTHM Health) The discussion breaks down: What AI is already doing inside healthcare (and why that's different from what you use at home)  Why tools like ChatGPT, Claude, and Gemini are not medical devices The risks of trusting AI-generated answers for diagnosis and treatment  How specialized tools like RTHM Intelligence attempt to address these gaps  The privacy trade-offs when sharing your medical data with AI  How to use AI safely and effectively as a patient This episode is a practical framework for using AI as a thinking partner—not a decision maker.⏱️ Chapter Markers00:00 – Why patients are turning to AI 03:15 – How AI is actually used in healthcare (FDA perspective) 07:30 – Why ChatGPT and similar tools aren't medical devices 15:30 – Specialized AI tools vs general AI (RTHM example) 24:50 – Risks: hallucinations, bad advice, and doctor tension 26:30 – Privacy and HIPAA: what happens to your data 38:25 – How to use AI safely in your careSupport the showSubscribe for free written summaries of each episode, resources, and more.  LongCovidMD.substack.com/subscribeSupport by donating at BuyMeACoffee

My team builds you a $167K/month client machine in 8 weeks. You just approve it. Interested?  → https://jointherainmakers.com/choosetime?utm_src=organicyoutube AI is killing your coaching business. Not because you're not using it. Because you're using it the same way everyone else is, and it's making you invisible. n this video, I'm breaking down the 3 biggest mistakes coaches make with AI that are costing them clients, killing their conversions, and turning their marketing into generic noise. And I'm showing you exactly how to fix it. Most coaches think they're ahead because they're using ChatGPT to create content. They're posting more. They're "automating." They feel productive. But productive and profitable are not the same thing. And while you're churning out AI-generated posts that sound like everyone else, coaches who understand how to use AI strategically are taking your clients. Here's what you're getting in this video: Why AI-generated content is making you invisible (and what to do instead) The 3 AI mistakes that are costing you thousands per month in invisible losses How to use AI to sharpen your positioning instead of just producing more content The hierarchy of what to automate first (most coaches have this backwards) Why ChatGPT is not your business partner and what to use instead The difference between generic AI tools and purpose-built AI systems for coaching businesses How the coaches pulling away from the pack are using AI right now I've audited over 100 coaching businesses in the last 3 months. The pattern is clear: coaches who adopted AI early thought they were ahead, and now their content gets ignored, their funnels convert worse, and their sales calls have dried up. It's not an AI problem. It's a strategy problem. The coaches winning right now aren't the ones with the flashiest AI setup. They're the ones who automated the boring, high-leverage stuff that directly touches revenue. Speed to lead. Sales prep. Delivery admin. The things that actually make money. I'm Chris Dufey, founder of The Rainmakers. We build full marketing ecosystems for coaches and consultants. I've built and sold a multi-7-figure coaching business, been behind the scenes of 7, 8, and 9-figure online businesses, and right now we're deep in the trenches building AI-powered systems that actually move the needle. I started sleeping on a couch with my baby daughter, wondering how I'd make rent. Built my first business to multi-7-figures, sold it, then spent years getting paid $25K/month retainers to fix some of the biggest info-businesses on the planet. Scaled my consulting offer to $1M/year in 63 days. Launched an agency, took it to $200K/month in under 90 days, then burned it all down because "bigger" doesn't always mean "better." Now I live between Bali and Australia with my four daughters, work 2-4 hours a day, and help coaches install the systems that gave me my freedom back. The window where "using AI" is enough is closing fast. The question isn't whether you're using it. It's whether you're using it in a way that makes you uncopyable. Instagram: https://www.instagram.com/wearetherainmakers Facebook: https://www.facebook.com/wearetherainmakers LinkedIn: https://www.linkedin.com/company/the-rainmakers Website: https://wearetherainmakers.com

My team builds you a $167K/month client machine in 8 weeks.  You just approve it. Interested? → https://jointherainmakers.com/chooset...  AI is killing your coaching business. Not because you're not using it. Because you're using it the same way everyone else is, and it's making you invisible. n this video, I'm breaking down the 3 biggest mistakes coaches make with AI that are costing them clients, killing their conversions, and turning their marketing into generic noise. And I'm showing you exactly how to fix it. Most coaches think they're ahead because they're using ChatGPT to create content. They're posting more. They're "automating." They feel productive. But productive and profitable are not the same thing. And while you're churning out AI-generated posts that sound like everyone else, coaches who understand how to use AI strategically are taking your clients. Here's what you're getting in this video: Why AI-generated content is making you invisible (and what to do instead) The 3 AI mistakes that are costing you thousands per month in invisible losses How to use AI to sharpen your positioning instead of just producing more content The hierarchy of what to automate first (most coaches have this backwards) Why ChatGPT is not your business partner and what to use instead The difference between generic AI tools and purpose-built AI systems for coaching businesses How the coaches pulling away from the pack are using AI right now I've audited over 100 coaching businesses in the last 3 months. The pattern is clear: coaches who adopted AI early thought they were ahead, and now their content gets ignored, their funnels convert worse, and their sales calls have dried up. It's not an AI problem. It's a strategy problem. The coaches winning right now aren't the ones with the flashiest AI setup. They're the ones who automated the boring, high-leverage stuff that directly touches revenue. Speed to lead. Sales prep. Delivery admin. The things that actually make money. I'm Chris Dufey, founder of The Rainmakers. We build full marketing ecosystems for coaches and consultants. I've built and sold a multi-7-figure coaching business, been behind the scenes of 7, 8, and 9-figure online businesses, and right now we're deep in the trenches building AI-powered systems that actually move the needle. I started sleeping on a couch with my baby daughter, wondering how I'd make rent. Built my first business to multi-7-figures, sold it, then spent years getting paid $25K/month retainers to fix some of the biggest info-businesses on the planet. Scaled my consulting offer to $1M/year in 63 days. Launched an agency, took it to $200K/month in under 90 days, then burned it all down because "bigger" doesn't always mean "better." Now I live between Bali and Australia with my four daughters, work 2-4 hours a day, and help coaches install the systems that gave me my freedom back. The window where "using AI" is enough is closing fast. The question isn't whether you're using it. It's whether you're using it in a way that makes you uncopyable.  Instagram: https://www.instagram.com/wearetherainmakers Facebook: https://www.facebook.com/wearetherainmakers LinkedIn: https://www.linkedin.com/company/the-rainmakers Website: https://wearetherainmakers.com

Zapier is doing hundreds of millions in ARR, has 800 employees, and has more AI agents than people. That ratio isn't an accident. Wade Foster, CEO and co-founder of Zapier, built one of the most capital-efficient software companies in history on less than $1 million in venture funding. When GPT-4 launched in March 2023, he called a company-wide "code red" (a term he'd never used before) and stopped the entire company for a week-long hackathon. What happened next reshaped how Zapier hires, operates, prices its product, and thinks about the future of software. In this episode of Founded & Funded, Karan Mehandru sits down with Wade to unpack: Why ChatGPT didn't trigger urgency at Zapier, but GPT-4 did — and the specific signal Wade used to make that call How Zapier went from 10% AI tool adoption to 90%+ across the company in a single week The pricing overhaul that simplified Zapier's model around task-based usage and why agents made seat-based pricing structurally broken Why Zapier's head of HR became the Chief People and AI Transformation Officer, and what that reveals about who actually leads change inside organizations The "build first, run always" framework Wade uses for deploying AI agents safely inside enterprise workflows For founders and operators navigating their own AI transformation, this is a practical, unfiltered look at what it actually takes from a CEO who's in the middle of it. Full Transcript: https://www.madrona.com/zapier-has-more-ai-agents-than-employees-heres-how-that-happened Chapters:  (00:00) – Introduction (01:44) – Zapier Today: Hundreds of Millions ARR, 800 Employees, More Agents Than People (06:16) – Why ChatGPT Didn't Trigger Urgency — But GPT-4 Six Months Later Did (07:12) – Code Red: Stopping the Entire Company for a Week-Long AI Hackathon (08:00) – How Zapier Moved AI Adoption From 10% to 90% of Employees in One Week (10:11) – Managing the Psychology of Change When Numbers Still Look "Okay" (13:11) – Why Companies in the Middle Ground Face the Hardest AI Transformation Problem (15:01) – From Bottoms-Up Adoption to Systematic ROI: The Two-Phase AI Rollout (17:00) – Why Zapier's Head of HR Became the Chief AI Transformation Officer (19:54) – Refactoring the Legacy Monolith: Writing Code for Agents Instead of Humans (21:25) – Zapier's Pricing Overhaul: Why Task-Based Usage Beats Seat Pricing (23:26) – Why Agents Will Choose What Software to Buy — and What That Does to SaaS (28:03) – The Build-First, Run-Always Framework for Enterprise Agent Governance (29:40) – Wade's AI Hiring War Council: A Multi-Agent System He Built That Morning (34:14) – The Leadership Profile That Thrives When the Job Is Rebuilding, Not Scaling (40:08) – If You're Starting a Company Today, Distribution Is the Bottleneck (42:10) – Why AI Deals Are Hard to Renew and the Rise of the Field Delivery Engineer

If you have been curious about AI but have no idea where to start, or you have been avoiding it altogether because it feels overwhelming or risky, this episode is for you. I am walking you through everything a counsellor, psychologist, or social worker in private practice needs to know about AI right now. What it actually is in plain English, what ChatGPT and Claude are and how they are different, the fears I hear from therapists every single week and which ones are worth taking seriously, the mistakes that are happening in real time that you want to avoid, and the genuine possibilities sitting right in front of you. And I am celebrating the launch of something I have been building for months: the Private Practice Resource Hub. A dedicated space where therapists can explore AI, access practical tools to help fill their diary and market their practice, learn the basics, and get training when they are ready. This is not a techy episode. It is a real conversation about a shift that is already happening, and how you can be part of it.   IN THIS EPISODE What AI actually is, explained without jargon or overwhelm. Why ChatGPT and Claude are the two tools I recommend for therapists in private practice and how they differ from each other. The fears therapists have about AI, including confidentiality, authenticity, and being replaced, and what to do with each of them. The mistakes that are already happening that could put your ethics and your reputation at risk. The time-saving possibilities that are available to you right now in your business. And a full introduction to the Private Practice Resource Hub, newly launched and built specifically for therapists.     THE ONE RULE YOU NEED TO REMEMBER Never put client information into an AI tool. Not names, not identifying details, not session content. Your ethical obligations do not pause because a tool is convenient. AI is for the business side of your practice, not the clinical side. Everything else is an opportunity.   WHAT IS THE PRIVATE PRACTICE RESOURCE HUB? The Private Practice Resource Hub is a dedicated space for therapists in private practice who want to explore AI, access ready-to-use tools, build their knowledge, and learn at their own pace. Inside you will find practical tools built to help you fill your diary and market your practice, AI basics content, and training you can access when you are ready. It is designed to meet you where you are, whether you are brand new to AI or already dabbling and want to go deeper. Join the Hub here: https://www.skool.com/private-practice-resource-hub   LINKS MENTIONED IN THIS EPISODE Private Practice Resource Hub: https://www.skool.com/private-practice-resource-hub Website: https://brooklynstorme.com Free Private Practice Marketing Quiz: https://brooklyn.myflodesk.com/moreclients Free Private Practice Quiz: https://brooklyn.myflodesk.com/pmquiz Website Wellness Check: https://sales.brooklynstorme.com/wwc-aud/ AI Playbook for Therapists: https://sales.brooklynstorme.com/aiplaybook/     CONNECT WITH ME Website: https://brooklynstorme.com Podcast: https://brooklynstorme.podbean.com/ Facebook Business Page: https://www.facebook.com/brooklynstormephd/ Facebook Profile: https://www.facebook.com/drbrooklynstorme/   ABOUT DR BROOKLYN STORME I am Dr Brooklyn Storme PhD, a business coach for counsellors, psychologists, and social workers in Australia who want to start, grow, and scale their private practice. I have over 30 years of experience in private practice and I am passionate about helping therapists build sustainable, profitable businesses without burning out. The Practice Momentum Private Practice Podcast is in the top 5% of podcasts globally with 500+ episodes.     AI in private practice, ChatGPT for therapists, Claude AI for counsellors, artificial intelligence therapy, private practice marketing Australia, counsellor business tools, psychologist private practice, social worker private practice, AI tools mental health professionals, Private Practice Resource Hub, AI ethics therapists, client confidentiality AI, practice momentum podcast, Dr Brooklyn Storme, fill your therapy diary, therapy business automation, AI content creation therapists, grow private practice Australia, ChatGPT prompts for therapists, private practice business coach

Grab our High-Converting Proposal Template and learn what to include (and what to leave out) to turn more of your proposals into higher-paying clients. ⁠https://webdesigneracademy.com/template⁠ February 2026 Income Report: $26K, Money Mindset, and the Scary Things Worth Doing In this episode, I pull back the curtain on February 2026... $26,735 in revenue, $23,015 in expenses, and everything behind the numbers. This isn't just a money update. It's a real look at the mindset work, the pivots, and the moves that felt terrifying and necessary at the same time. From the discovery that writing is my queen bee role, to the decision to release a free Package Matrix™ template, to finally launching on TikTok after years of avoidance... this episode covers the real behind-the-scenes of running a profitable business when your thoughts about money are still a work in progress. What You'll Learn Why perfectionism keeps prices low and how to spot the pattern in yourself Why using ChatGPT for mindset coaching can create an echo chamber that makes things worse What a queen bee role is and how identifying yours can change your business Why Shannon is releasing a free Package Matrix™ template and what fear I had to work through to do it How to identify your "hidden competing commitments" that are blocking you from doing the scary thing Key Timestamps [00:01] Welcome & what money mindset really means [07:20] Why ChatGPT is a problematic mindset coach [10:43] February inflow, outflow and overflow [18:33] Queen bee role discovery [37:19] The Package Matrix™ free template [41:26] Why I'm finally on TikTok @profitablewebdesigner Resources Mentioned ⁠Web Designer Academy⁠ - Business coaching programs for women web designers ⁠Next Level Mastermind⁠ - For experienced web designers ready for their next level ⁠Simply Profitable Designer Summit⁠ - Free annual summit for web designers ⁠5 Proposal Mistakes Guide⁠ - Free guide Related Episodes ⁠Episode 183: Pricing Strategy: Inside The Package Matrix Framework⁠ ⁠Episode 182: January 2026 Income Report⁠ ⁠Episode 180: The Truth About Pricing with Melina Palmer⁠ About Shannon Mattern Shannon Mattern is a Pricing Strategist and the founder of the Web Designer Academy where she helps experienced women web designers book higher-paying web design projects, charge more with confidence, run projects without overworking and burnout and break through to their next level of income and freedom. For Web Designers: ⁠https://webdesigneracademy.com⁠ For Service Providers, Consultants & Agencies: ⁠https://shannonmattern.com⁠ Tiktok: @profitablewebdesigner | @shannonlmattern

Grab our High-Converting Proposal Template and learn what to include (and what to leave out) to turn more of your proposals into higher-paying clients. https://webdesigneracademy.com/template February 2026 Income Report: $26K, Money Mindset, and the Scary Things Worth Doing In this episode, I pull back the curtain on February 2026... $26,735 in revenue, $23,015 in expenses, and everything behind the numbers. This isn't just a money update. It's a real look at the mindset work, the pivots, and the moves that felt terrifying and necessary at the same time. From the discovery that writing is my queen bee role, to the decision to release a free Package Matrix™ template, to finally launching on TikTok after years of avoidance... this episode covers the real behind-the-scenes of running a profitable business when your thoughts about money are still a work in progress. What You'll Learn Why perfectionism keeps prices low and how to spot the pattern in yourself Why using ChatGPT for mindset coaching can create an echo chamber that makes things worse What a queen bee role is and how identifying yours can change your business Why Shannon is releasing a free Package Matrix™ template and what fear I had to work through to do it How to identify your "hidden competing commitments" that are blocking you from doing the scary thing Key Timestamps [00:01] Welcome & what money mindset really means [07:20] Why ChatGPT is a problematic mindset coach [10:43] February inflow, outflow and overflow [18:33] Queen bee role discovery [37:19] The Package Matrix™ free template [41:26] Why I'm finally on TikTok @profitablewebdesigner Resources Mentioned Web Designer Academy - Business coaching programs for women web designers Next Level Mastermind - For experienced web designers ready for their next level Simply Profitable Designer Summit - Free annual summit for web designers 5 Proposal Mistakes Guide - Free guide Related Episodes Episode 183: Pricing Strategy: Inside The Package Matrix Framework Episode 182: January 2026 Income Report Episode 180: The Truth About Pricing with Melina Palmer About Shannon Mattern Shannon Mattern is a Pricing Strategist and the founder of the Web Designer Academy where she helps experienced women web designers book higher-paying web design projects, charge more with confidence, run projects without overworking and burnout and break through to their next level of income and freedom. For Web Designers: https://webdesigneracademy.com For Service Providers, Consultants & Agencies: https://shannonmattern.com Tiktok: @profitablewebdesigner | @shannonlmattern

In this video, we break down what OpenAI's new ChatGPT ads mean for B2B marketers, and why it's probably not the performance channel you should be betting on in 2026.We cover:→ The background behind OpenAI's push into advertising and why they're starting with B2C→ Why ChatGPT ads are unlikely to impact B2B marketing anytime soon (ad-free business/enterprise tiers, limited tracking, shopping-focused formats)→ How buyer discovery is shifting from search + social toward AI assistants→ Our 3-point framework (based on the Five B's) to prepare your B2B marketing for the age of LLMsIf you are a B2B founder or marketer wondering whether you should be spending on ChatGPT ads, or trying to figure out how to get your brand surfaced in AI-driven search results, this episode is for you.Tune in and learn:✅ Why the fundamentals of trust-building and demand gen matter more than ever✅ How to tighten your ICP messaging so LLMs actually surface your brand✅ The content and distribution strategies that build organic visibility in ChatGPT✅ How to set up measurement and UTM practices now so you're ready when B2B ads do arrive-----------------------------------------------------

ChatGPT has pulled back from in-chat instant checkout — and the decision says a lot about where AI-powered commerce is actually heading.Rather than closing sales inside the conversation, OpenAI is shifting focus to app-based retailer connections, effectively acknowledging that consumer trust isn't there yet. Meanwhile, Google is doubling down on agentic commerce with cart functionality, full product catalogues, loyalty point integration, and one-click account creation — all within AI Mode.The contrast couldn't be sharper. And for e-commerce marketers, understanding what's really happening here is critical.Charlie Marchant (CEO of Exposure Ninja) and Dale Davies (Head of Marketing at Exposure Ninja) break down everything they know:Why ChatGPT's in-chat checkout was always going to struggle — and what the Shopify stat that likely triggered the retreat actually tells usHow ChatGPT is being used as a top-of-funnel discovery tool, and why users are completing purchases on Google insteadThe real conversion rate opportunity hiding in your ChatGPT traffic right now (hint: a UK window installer is seeing 7.3% conversion from ChatGPT versus 4.5% from organic search)Why Google has the trust advantage in agentic commerce — and how far ahead it already isWhere e-commerce marketers should be focusing their limited resource: informational content, product pages, or digital PRThe technical mistake even large brands are making that completely blocks them from AI search resultsCharlie's three key takeaways from speaking at eCommerce Scotland — including the Cloudflare configuration issue that's silently killing visibility for major retailersIf you run or market an eCommerce business, this episode cuts through the noise on where AI commerce is genuinely headed — and what you should be doing about it now, not when the dust settles.Follow Charlie Marchant on LinkedIn for the latest in AI Search Optimisation:https://www.linkedin.com/in/charliemarchant/Request a free marketing review:https://exposureninja.com/reviewTry Semrush for FREE:https://thankyouninjas.comListen to these episodes next:The BEST SEO Strategies for 2026https://exposureninja.com/podcast/368/The BEST AI Search Optimisation Strategies for 2026https://exposureninja.com/podcast/366/If My SEO Traffic Dropped, Here's Exactly What I'd Do https://exposureninja.com/podcast/378/

If you've been using ChatGPT and you're thinking about switching to Claude or another AI model, the biggest obstacle isn't the technology. It's the time you've already invested. This episode covers why people are leaving ChatGPT right now, why staying on the sidelines entirely isn't a real option, and the practical methods available for moving your personalization and workflows from one AI model to another without starting from scratch.Timestamps[0:00] Introduction[1:15] Why people are switching away from ChatGPT[3:30] The AI ethics and military controversy[5:45] Why ChatGPT is better at design (and the tradeoff behind it)[11:00] Why avoiding AI altogether isn't a real option[13:45] The challenge of switching[16:00] Method one: copy and paste a single chat[17:30] Method two: migrate project rules[19:00] Method three: account vs. memory import[21:15] How to ask AI for help switchingThe Claude Memory import feature!https://medium.com/ai-software-engineer/claude-just-launched-memory-import-now-you-can-cancel-chatgpt-faster-67d53ebacddb -- CONTACTLeave Feedback or Request Topics:https://forms.gle/bqxbwDWBySoiUYxL7

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

AI is changing how shoppers discover products, and Amazon sellers need to pay attention now. In this episode, Scott breaks down the rise of AI-driven product discovery through tools like Amazon Rufus and ChatGPT, and explains why visibility in AI answers is becoming a new layer of competition for sellers. He unpacks Amazon's Cosmo framework, including the key product-understanding questions AI systems use to evaluate listings, and introduces SmartScout's new tools built for this shift: the Amazon AI Scorecard and the AI Visibility Monitor. Scott explains how the scorecard audits your listing content across bullets, A+ content, and images to measure how well your product answers AI-relevant questions. He also shows how the visibility monitor tracks how often your products appear in ChatGPT recommendations over time, even when AI responses are inconsistent. Scott also shares how sellers can improve AI visibility through better listing content, stronger online presence, and a more intentional long-term strategy for LLM discovery. If you want to know whether your brand is winning the AI visibility race in your category, this episode lays out the framework. Episode Notes: 02:00 - Amazon Rufus adoption and what it could mean for product discovery 03:10 - ChatGPT shopping behavior and why AI shopping queries still matter 04:06 - Why AI shopping accuracy is not perfect yet, but still important 04:34 - Amazon Cosmo and the product questions AI systems use to understand listings 07:00 - The shift from keyword-only thinking to AI-ready product content 07:32 - SmartScout's Amazon AI Scorecard and how it evaluates listing quality 08:10 - How the scorecard creates a feedback loop for continuous improvement 10:23 - SmartScout's AI Visibility Monitor and tracking LLM recommendation share 12:40 - Why ChatGPT results are non-deterministic and how visibility percentage helps 14:53 - Creatine example: measuring AI visibility by niche and query type 16:23 - How to improve AI visibility through listing content and off-Amazon signals 17:44 - Why this matters for sellers, brands, and teams in 2026   Related Post Top 10 Amazon FBA Reimbursement Services to Recover Your Funds   Scott's Links: LinkedIn: linkedin.com/in/scott-needham-a8b39813 X: @itsScottNeedham Instagram: @smartestseller YouTube: www.youtube.com/@smartestamazonseller2371 Newsletter: https://www.smartscout.com/newsletter-sign-up • • Blog: https://www.smartscout.com/blog

Are you using AI… or letting AI use you?

This week's podcast is about the big release of Seedance 2.0 by Bytedance.You can listen to this podcast here, which has the slides and graphics mentioned. Also available at iTunes and Google Podcasts.Here is the link to the TechMoat Consulting.Here is the link to our Tech Tours.Here are some videos I made (here).Here are the winners:Viewers. It's amazing. GPUs and data centers. Plus energy providers. IP holders that get lots of attention.   Independent creators who will get lots attention and creative satisfaction. Business content creators - especially in ads and content. Platform biz models. Audience builders like YouTube and TikTok. Plus marketplaces like Taobao.iQiyi and combinations of streaming and audience builders.Netflix and pure streamers (maybe). Here are the losers:Most professional production companies. Most tv and film studios. Basically, any business that has been relying on scale in content creation. Ad agencies focused on content creation.Individuals and firms with specialized skills related to tv and film production.Independent content creators trying to monetize Los Angeles?Hollywood's managerial class. Political activists embedded in entertainment.Here are my past articles / podcasts on this:Why ChatGPT and Generative AI Are a Mortal Threat to Disney, Netflix and Most Hollywood Studios (Tech Strategy – Podcast 150)How Generative AI Is Going to Disrupt YouTube and TikTok (Tech Strategy – Podcast 152). Jan 2023How Generative AI Services Are Disrupting Platform Business Models (1 of 2) (Tech Strategy – Daily Article)-------I am a consultant and keynote speaker on how to increase digital growth and strengthen digital AI moats.I am the founder of TechMoat Consulting, a consulting firm specialized in how to increase digital growth and strengthen digital AI moats. Get in touch here.I write about digital growth and digital AI strategy. With 3 best selling books and +2.9M followers on LinkedIn. You can read my writing at the free email below.Note: This content (articles, podcasts, website info) is not investment advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. Investing is rSupport the show

In this episode, Jeff Mains sits down with KG Charles-Harris, a serial entrepreneur who has founded six companies across industries ranging from genomics to AI. KG is the founder and CEO of Quarrio, a deterministic AI platform that solves a critical problem: getting accurate, consistent answers from corporate data in seconds instead of weeks.KG shares his unconventional path to entrepreneurship, explaining how his companies emerge from late-night conversations with brilliant people who share a common problem. He breaks down the crucial difference between deterministic and probabilistic AI systems, making the case that when decisions involve real money, real lives, or real consequences, accuracy isn't optional—it's essential.Key Takeaways[0:00] Introduction to KG Charles-Harris and his multi-industry entrepreneurial journey[1:18] How companies are born from conversations: The pattern behind KG's six startups[2:30] The genomics company origin story: From 4:30 AM conversation to Norwegian startup[3:28] Why Quarrio exists: Even data company CEOs can't get the data they need[4:31] The Quarrio platform: 100% accuracy, plain language queries, auto-visualization[5:27] Real-world impact: The $60M margin leak that took two quarters to find (would take 5 seconds with Quarrio)[7:00] Deterministic vs. probabilistic AI explained: Why autopilots don't hallucinate[11:30] The cycle time framework: Information → Decision → Action → Results[13:00] Why ChatGPT's inconsistency is a dealbreaker for enterprise decisions[18:30] Organizations as "decision-making machines" and democratizing decisions to every level[20:30] The data explosion: Managing 300+ structured data sources in mid-sized enterprises[23:00] Why Quarrio focuses on structured enterprise data (SAP, Salesforce, Oracle) instead of PDFs[30:00] Go-to-market strategy: Why they started with Salesforce and sales teams[32:30] The Salesforce incubation story: Free office space and immediate investment[33:30] Team building philosophy: Surrounding yourself with people smarter than you[37:00] Stewardship as core ethos: Taking care of family, team, customers, and partners[38:30] The founder's dilemma: Resilience vs. delusion—knowing when to persist[43:00] Where to connect with KG and learn more about QuarrioTweetable Quotes"An organization is essentially a machine for making decisions and taking actions that have certain types of results." — KG Charles-Harris"Cycle time to information shortens cycle time to decision, which shortens cycle time to action, which shortens cycle time to results." — KG Charles-Harris"Agentic AI without context is useless. You need determinism to trust what is enacted within your system." — KG Charles-Harris"Effectiveness requires redundancy. Efficiency optimizes for the shortest time or best expense, but effectiveness accomplishes the goal." — KG Charles-Harris"I'm not very smart, and because I realize that, I ensure I work with people who are very smart. Then they make me look smart." — KG Charles-Harris"Most of us give up before we should have. The break would have come had we stuck it out one more month." — KG Charles-Harris"If you don't have their back, you cannot expect them to have yours. It's a

In this episode of Diversified Game, Kellen Coleman sits down with Santiago A. Cueto, Esq., best selling author of Winning Lawsuits in Florida and founder of Cueto Law Group, to expose the legal mistakes that quietly destroy businesses, contracts, and wealth.If you are a Florida business owner, entrepreneur, real estate investor, or executive, this conversation can save you thousands or even millions by helping you avoid lawsuits before they start.Santiago breaks down:• The hidden contract clauses most people never read• Why jurisdiction and venue can decide your case before court• Why ChatGPT generated contracts are dangerous• Why “sovereign citizen” arguments never work• How honesty with your attorney can make or break your case• The difference between real legal strategy and internet advice• International business disputes and when you actually need a lawyerThis episode is education, not entertainment.Santiago A. Cueto, Esq.Founder, Cueto Law GroupAuthor, Winning Lawsuits in Florida

The firms winning with AI aren't the ones implementing every tool they can find. They're the ones being strategic about what actually moves the needle.Rachel Ferris learned this firsthand when she built TaxStack AI, not because she wanted to start a software company, but because she needed a solution that didn't exist. As a tax advisor specializing in Puerto Rico's Act 60, she was spending hours researching complex state-specific cases. ChatGPT seemed like the answer, until it started citing Reddit and Quora as sources. When she'd call it out, it would cheerfully admit, "You're right, that's actually not correct." That's when she realized: if she wanted AI that actually worked for tax research, she'd have to build it herself.In this episode, Rachel shares how she created a platform that sources directly from actual tax code, IRS forms, and treasury regulations, so you get accurate answers with citations you can trust. But more importantly, she breaks down the difference between firms that will thrive with AI and firms that will struggle: it's not about adopting everything, it's about adopting responsibly.The conversation covers:Why ChatGPT's fact-check problem makes it dangerous for tax research (and how to fix it by sourcing your own database)The cautionary tale of firms that went all-in on AI integration in 2024, only to abandon everything six months later Why now is the time to pause and evaluate rather than frantically adopt every AI tool that hits the marketHow AI should free you up for higher-value advisory work with existing clients, not just help you take on more volumeEd Kless's "transformation economy" concept: moving from providing transactions to providing transformationsThe three-pillar approach to attracting and retaining young talent: technology + mentorship + entrepreneurship mindsetWhy mandatory mentorship programs fail (and what actually works instead)How being genuinely curious about people has opened more doors than any technical skill Rachel has learnedRachel also breaks down why firms that use AI responsibly will win, while firms that use it to cut personality and human connection will lose clients. She explains why young people crave personal connection more than you think, why entrepreneurial-minded accountants connect better with business owner clients, and how spending three hours talking to a Pizza Hut franchise owner taught her more about business than any textbook.Download Now: https://poegroupadvisors.com/accounting-practice-academy/increase-letter/Price increases are nothing to fear. The real challenge is effectively informing clients of these changes. Our templates will help you demonstrate your value and help clients understand the increases necessary to keep your firm afloat.*Download now and receive:*- (1) Major Fee Increase Letter Template- (1) 20% Fee Increase Letter Template

In this fiery solo episode, Dani rips the mask off ChatGPT and exposes how “neutral” AI quietly smuggles ideology, division, revisionist history, identity politics, and activist talking points into its super very editorialized responses to otherwise simple factual questions. Using a real-time AI exchange, she breaks down how propaganda now operates one-on-one, why unsolicited “context” is narrative control, and how language policing, emotional management, and academic activism are reshaping how we think, speak, and perceive reality. From mass attention engineering in sports stadiums to everyday dinner-table censorship, this episode is a sharp, bold call to reclaim discernment, expand our comfort zones, and stop outsourcing our thinking to machines—or to the people trained/indoctrinated by them.Watch on Odysee. Listen on Progressive Radio Network and podcast platforms everywhere.Part 2:danikatz.locals.comwww.patreon.com/danikatzAll things Dani, including books, courses, coaching + consulting, and her one-of-a-kind, critically acclaimed POP PROPAGANDA DIGITAL MEDIA LITERACY COURSE:www.danikatz.comPlus, schwag:danikatz.threadless.comRegister now for my FREE Pop Propaganda webinar on January 8th:https://www.eventbrite.com/e/how-to-win-the-war-on-our-minds-a-free-pop-propaganda-webinar-tickets-1977273627647?aff=oddtdtcreatorChemical Free Body products, including Parasite Free formula:https://www.chemicalfreebody.com/DANI28299Justice for Sale article:https://estancia.news/justice-for-sale/Show notes:• Why ChatGPT is not a neutral research tool• A real example of AI inserting ideological framing into a simple historical question• How moral language, “context,” and activist narratives get smuggled into AI responses• The difference between facts, interpretation, and propaganda• Why AI-driven one-on-one conditioning is more dangerous than mass media• Attention engineering in stadiums, screens, and collective environments• The psychology of gaslighting, flattery, and narrative management• How fear and arrogance shut down curiosity and honest inquiry• The rise of language policing and over-somatization in social spaces• Why passionate expression is not pathology—and shouldn't be treated as such• Discernment as the antidote to ideological overreach• Invitation to the free Pop Propaganda live webinar

The generative AI landscape has shifted again, and at speed. In this update episode, Daniel Rowles breaks down a rapid sequence of releases from OpenAI and Google, explaining what has actually changed, what genuinely matters, and how marketers and business leaders should respond. Covering everything from ChatGPT 5.x and Gemini 3 through to image generation breakthroughs, AI-powered apps, and the rise of accessible 'vibe coding', this episode is a practical briefing rather than hype-driven commentary. Daniel focuses on real-world capability, usability, and where these tools are already changing how marketing teams work, build, and analyse. Rather than asking which model is "winning", the episode reframes the question around task-based selection. Different tools now excel at different jobs, from coding and image editing to workflow automation and reporting. Understanding those strengths is quickly becoming a competitive advantage In This Episode How the AI model race intensified between OpenAI and Google at the beginning of 2026 What actually changed with ChatGPT 5.1 and 5.2, beyond the headline claims Why Gemini 3 represents a major leap in reasoning and coding capability How image generation tools have crossed a new threshold in photorealism and editing What Nano Banana Pro means for branded, on-style visual creation Why ChatGPT connectors becoming "apps" is more important than it sounds How tools like Canva, Adobe, and Figma now integrate directly into AI workflows What vibe coding really is, and why it matters for non-technical marketers How canvas mode transforms the way code, content, and interfaces are created Why AI-powered browsers and agent modes could redefine reporting and analysis Key Takeaways No single AI model is best at everything, and choosing by task is now essential Image editing and brand-consistent visuals are becoming dramatically more accessible Marketers can now create interactive tools and content without traditional development AI-assisted coding is shifting from novelty to practical everyday use Workflow automation and reporting are emerging as some of the biggest wins Understanding interfaces and deployment matters as much as model intelligence

Discover why disciplined due diligence beats FOMO, how to wield AI without losing human insight, why “cash is king” again, and what the rise of lean micro-unicorns means for VCs, family offices, and startups.   In this episode of the Registered Investment Advisor Podcast, Seth Greene interviews Daniel Nikic, Global Investment Specialist and Founder of COHRES, who explains why disciplined due diligence beats FOMO, how AI should augment human insight, and why “cash is king” again. He shares how COHRES tailors research for VCs, family offices, and startups, forecasts durable trends in AI, energy, healthcare, and data, and explores the rise of lean micro-unicorns. Nikic also details the operational pivots behind scaling a boutique firm toward AI-enabled analysis while aligning execution with investor priorities.   Key Takeaways: → How the most expensive errors are chasing hype, skipping time-horizon work, and forgetting that once capital leaves your account, there's no guarantee it returns. → Why ChatGPT-level answers aren't enough for funds. → How big brands will stay big and emerging funds with DPI will struggle to raise. → Why direct on-call collaboration provides guidance when speed matters. → How having a global perspective allows for tailored insights to what each investor treats as a green flag or a deal breaker.   Daniel Nikic is a global investment research expert and entrepreneur with over a decade of experience advising investors, high-net-worth individuals, enterprises, and entrepreneurs. Raised in Canada and now based in Croatia, Daniel specializes in global markets, focusing on the U.S., European, and Middle Eastern financial landscapes. As the founder of COHRES, Daniel has analyzed over 15,000 companies across sectors like AI, software, and data. He delivers high-quality market research, financial management, due diligence, and AI data auditing to empower clients to make strategic, informed decisions. Known for his collaborative approach, Daniel builds long-term relationships rooted in trust and shared success. Daniel is also passionate about mentoring early-stage entrepreneurs, guiding them on market strategies, innovation, and business growth. His expertise in global markets and emerging trends makes him a trusted advisor in the financial sector.   Connect With Daniel:   Website: https://www.danielnikic.com/ https://cohres.com/ X: https://x.com/DNikic87 Facebook: https://www.facebook.com/people/Daniel-Nikic/ LinkedIn: https://www.linkedin.com/in/daniel-nikic/   Learn more about your ad choices. Visit megaphone.fm/adchoices

Discover why disciplined due diligence beats FOMO, how to wield AI without losing human insight, why “cash is king” again, and what the rise of lean micro-unicorns means for VCs, family offices, and startups.   In this episode of the Registered Investment Advisor Podcast, Seth Greene interviews Daniel Nikic, Global Investment Specialist and Founder of COHRES, who explains why disciplined due diligence beats FOMO, how AI should augment human insight, and why “cash is king” again. He shares how COHRES tailors research for VCs, family offices, and startups, forecasts durable trends in AI, energy, healthcare, and data, and explores the rise of lean micro-unicorns. Nikic also details the operational pivots behind scaling a boutique firm toward AI-enabled analysis while aligning execution with investor priorities.   Key Takeaways: → How the most expensive errors are chasing hype, skipping time-horizon work, and forgetting that once capital leaves your account, there's no guarantee it returns. → Why ChatGPT-level answers aren't enough for funds. → How big brands will stay big and emerging funds with DPI will struggle to raise. → Why direct on-call collaboration provides guidance when speed matters. → How having a global perspective allows for tailored insights to what each investor treats as a green flag or a deal breaker.   Daniel Nikic is a global investment research expert and entrepreneur with over a decade of experience advising investors, high-net-worth individuals, enterprises, and entrepreneurs. Raised in Canada and now based in Croatia, Daniel specializes in global markets, focusing on the U.S., European, and Middle Eastern financial landscapes. As the founder of COHRES, Daniel has analyzed over 15,000 companies across sectors like AI, software, and data. He delivers high-quality market research, financial management, due diligence, and AI data auditing to empower clients to make strategic, informed decisions. Known for his collaborative approach, Daniel builds long-term relationships rooted in trust and shared success. Daniel is also passionate about mentoring early-stage entrepreneurs, guiding them on market strategies, innovation, and business growth. His expertise in global markets and emerging trends makes him a trusted advisor in the financial sector.   Connect With Daniel:   Website: https://www.danielnikic.com/ https://cohres.com/ X: https://x.com/DNikic87 Facebook: https://www.facebook.com/people/Daniel-Nikic/ LinkedIn: https://www.linkedin.com/in/daniel-nikic/   Learn more about your ad choices. Visit megaphone.fm/adchoices

If you're not getting cited by ChatGPT, your “AI SEO” strategy isn't working, no matter what your dashboards say. Most of it is observability theater: dashboards, charts, synthetic prompts — and zero actual placement.In this episode, we chat with Shawn Schneider, founder of Eldil AI, about what actually determines whether your company shows up in ChatGPT answers. The short answer: LLMs don't reward more content, clever prompts, or prettier dashboards. They reward a small set of trusted third-party sources — and most brands aren't mentioned in any of them.Shawn breaks down why observability alone creates a false sense of progress, how to identify the specific citations that dominate your category, and how to turn that insight into real placements through outreach and negotiation. We also unpack why Google Search Console is still the best signal we have for AI-driven queries, how to prioritize the one citation that actually matters, and what the first 30–90 days can look like when you do this correctly.GuestShawn Schneider — founder of Eldil AI, a GEO / AI SEO platform focused on identifying and securing the citations LLMs rely on most; helps brands and agencies win visibility in ChatGPT by targeting the power-law sources that shape AI answers.Guest LinksLinkedIn: https://www.linkedin.com/in/shawn-schneider-61b2b5207/ Company Website: https://www.eldil.ai/What You'll LearnWhy most GEO / AI SEO observability tools are meaningless without actual placements The only thing that reliably improves AI search visibility: citation placementsHow to use Google Search Console to surface AI fan-out queriesWhy synthetic prompt data is still unreliable (and what to trust instead)The power law of citations: why only 1–3 sources actually matterHow Eldil turns citation discovery into outreach and negotiated placementsWhat 30–90 days can look like when you secure the right citationWhich industries should invest heavily — and which should ignore this for nowWhy ChatGPT dominates referral traffic compared to other LLMsWhat happens when ads arrive inside AI search resultsTimestamps00:00 — GEO, AI SEO, AEO: noise vs. reality00:21 — Why observability tools don't move the needle03:55 — Where GEO tools get their data (and why it's messy)07:16 — Using Google Search Console as a prompt proxy09:40 — The three pillars: technical, content, authority12:07 — Citations as the dominant ranking lever13:07 — The power law: thousands of citations, one winner19:07 — How fast results actually show up20:39 — When building your own citation content makes sense30:41 — Which business models win with GEO37:11 — ChatGPT ads and the future of AI search41:32 — Where to find Shawn and closing thoughts Key Topics & Ideas1. Why dashboards feel good but don't create outcomes.Most tools are essentially “Google Analytics for LLMs”ChatGPT referrals rise naturally as usage increasesCharts go up even if you do nothingWithout placements, observability is just vanity2. The three common approaches in the market today:Guessing prompts with LLMsClickstream data sourced from Chrome extensions and brokersSynthetic prompts without transparencyEldil uses Google Search Console + Analytics as the best available proxy for real intent.3. How to spot AI-generated fan-out queries:50+ character queriesHigh impressionsLow or zero clicksThese often represent LLMs expanding short prompts into long-form searches.4. The three pillars: Technical, Content, AuthorityTechnical — can an LLM crawl and understand your site?Content — does useful information exist?Authority — does anyone credible back it up?Authority is the multiplier most teams ignore.5. What actually shapes AI answers:Citations are not backlinks, they are semantic explanationsLLMs repeatedly return to the same trusted sourcesThird-party listicles and niche blogs dominate citation share6. The Power Law of Citations10k–15k citations may exist200–300 matter1–3 actually move the needleIf you're not in those, content volume won't save you.7. The real workflow:Identify high-value customer questionsExtract dominant citationsRank them by weightContact site ownersNegotiate placementMonitor AI visibility and referral trafficThis is where most tools stop — and where Eldil focuses.8. How many placements do you need?Surprisingly few.You don't need 100 placementsYou need the right oneThen expand into adjacent verticalsThis is concentrated betting, not spray-and-pray SEO.9. Why GEO feels different from traditional SEO:You are inserting into sources that already rankChanges can show up in weeks, not yearsMeaningful referral growth often appears within ~60–90 days10. Who Should (and Shouldn't) Do ThisBest fit:High-ACV B2B SaaSLong buying cyclesHigh-LTV e-commerce (supplements, skincare)ICPs that already live in ChatGPTIf your customers do not use LLMs yet, start elsewhere.11. Why ChatGPT is the main eventBased on Eldil's data:ChatGPT referrals dwarf Perplexity and othersFor most companies, this is where focus belongsSmaller channels still matter for high-ticket sales12. What's coming nextPaid placements inside LLMsOrganic plus paid becoming a one-two punchCitation inventory getting expensive fastThe window for cheap dominance will not last.SponsorToday's episode is brought to you by Graphed – an AI data analyst & BI platform.With Graphed you can:Connect data like GA4, Facebook Ads, HubSpot, Google Ads, Search Console, AmplitudeBuild interactive dashboards just by chatting (no Looker Studio/Tableau learning curve)Use it as your ETL + data warehouse + BI layer in one placeAsk:“Build me a stacked bar chart of new users vs. all users over time from GA4”…and Graphed just builds it for you.

In this episode of Excess Returns, we sit down with David Wright, Head of Quantitative Investing at Pictet Asset Management, for a deep and practical conversation about how artificial intelligence and machine learning are actually being used in real-world investment strategies. Rather than focusing on hype or black-box promises, David walks through how systematic investors combine human judgment, economic intuition, and machine learning models to forecast stock returns, construct portfolios, and manage risk. The discussion covers what AI can and cannot do in investing today, how machine learning differs from traditional factor models and large language models like ChatGPT, and why interpretability and robustness still matter. This episode is a must-watch for investors interested in quantitative investing, AI-driven ETFs, and the future of systematic portfolio construction.Main topics covered:What artificial intelligence and machine learning really mean in an investing contextHow machine learning models are trained to forecast relative stock returnsThe role of features, signals, and decision trees in quantitative investingKey differences between machine learning models and large language models like ChatGPTWhy interpretability and stability matter more than hype in AI investingHow human judgment and machine learning complement each other in portfolio managementData selection, feature engineering, and the trade-offs between traditional and alternative dataOverfitting, data mining concerns, and how professional investors build guardrailsTime horizons, rebalancing frequency, and transaction cost considerationsHow AI-driven strategies are implemented in diversified portfolios and ETFsThe future of AI in investing and what it means for investorsTimestamps:00:00 Introduction and overview of AI and machine learning in investing03:00 Defining artificial intelligence vs machine learning in finance05:00 How machine learning models are trained using financial data07:00 Machine learning vs ChatGPT and large language models for stock selection09:45 Decision trees and how machine learning makes forecasts12:00 Choosing data inputs: traditional data vs alternative data14:40 The role of economic intuition and explainability in quant models18:00 Time horizons and why machine learning works better at shorter horizons22:00 Can machine learning improve traditional factor investing24:00 Data mining, overfitting, and model robustness26:00 What humans do better than AI and where machines excel30:00 Feature importance, conditioning effects, and model structure32:00 Model retraining, stability, and long-term persistence36:00 The future of automation and human oversight in investing40:00 Why ChatGPT-style models struggle with portfolio construction45:00 Portfolio construction, diversification, and ETF implementation51:00 Rebalancing, transaction costs, and practical execution56:00 Surprising insights from machine learning models59:00 Closing lessons on investing and avoiding overtrading

Most of us walk around convinced we know our weaknesses, but what if the thing that knows you better than anyone (your AI assistant) could tell you what you're actually missing? We asked ChatGPT one brutal question and got answers that hit way too close to home. The uncomfortable truth: we're all playing smaller than we should, carrying more weight than we need to, and missing opportunities hiding in plain sight. In this episode, we test a viral prompt that reveals your blind spots, squandered potential, and the influence you didn't know you had...then process the existential crisis that follows.Key Episode Moments:The prompt that started it all: "Based on what you know about me, what are my blind spots?"Why ChatGPT knows you better than you think (and what that means)Jason gets told he's a Ferrari forcing everyone into a school busThe "super competent leader tax" — when being good at everything becomes the problemJeremy's revelation: treating creative work as a side hustle instead of the main platformImposter syndrome meets AI: "You're already operating at board level, stop asking permission"The technical vs. emotional problem-solving trap most high performers fall intoWhy "playing small" feels safer than taking the big swingChatGPT's productization challenge: you're giving away thousand-dollar consulting for freeThe Taylor Swift wisdom nobody expected: ruin the friendship, take the riskTimestamps:0:00 The prompt that started everything3:40 Jason's live AI assessment begins6:12 "You're a Ferrari and everyone else is in a school bus"9:01 The imposter syndrome AI detected immediately11:45 Why treating every problem as technical backfires14:27 The IP you're not monetizing (and should be)18:30 Jeremy's gut-punch realization about playing small21:35 How ChatGPT knows you better than you think24:36 Why failure beats decades of "what if"27:00 What to do with this uncomfortable informationMORE FROM BROBOTS:Get the Newsletter!Connect with us on Threads, Twitter, Instagram, Facebook, and TiktokSubscribe to BROBOTS on YoutubeJoin our community in the BROBOTS Facebook groupSafety/Disclaimer Note: This episode discusses using AI for self-reflection and personal assessment. Remember that AI tools provide perspective based on patterns in your usage—they're not substitutes for professional coaching, therapy, or mental health support. The hosts are sharing their personal experiences, not providing professional advice.

Join Simtheory: https://simtheory.ai (Use coupon BLACKFRIDAY15 for $15 USD off any subscription).----Simtheory Discord: https://discord.gg/Ar6GeQnAR7This Day in AI Discord: https://discord.gg/TVYH3HD6qsLinkedIn Group: https://www.linkedin.com/groups/16562039/Spotify: https://open.spotify.com/artist/28PU4ypB18QZTotml8tMDq?si=FPaJU2NRSnOSNPmnsfwA_g---CHAPTERS:00:00 Intro & Fatal Patricia Update01:40 Promotions (Discord, Black Friday, LinkedIn)04:36 Claude 4.5 Opus - Best Anthropic Model Ever?31:17 Computer Use API Updates36:14 Will AI Replace 57% of Jobs? (McKinsey Report)1:00:52 Claude 4.5 Opus Demos (Christmas Hut & Diss Track Preview)1:07:13 Microsoft Farah 7B - Moose Porn Refusals1:21:51 Why ChatGPT's MCP-UI Apps Are a Bad Idea1:42:01

You can talk about love, kids, and sex, but the second money comes up, things get awkward. This week on SUPERWOMEN, I sit down with Jackie Combs, Matrimonial & Family Law Partner at Blank Rome LLP. Jackie has seen firsthand what happens when women avoid talking about finances until it's too late. She's advised clients ranging from celebrities to founders, many of whom didn't realize what they were financially entitled to or what they'd already sacrificed. We discuss why prenups aren't just for the rich, how financial red flags can appear earlier than you think, and why it's never too soon to start these conversations. Whether you're just dating or have been married for ten years, you'll want to hear this. Episode Guide: (00:00) Meet Jackie Combs, Matrimonial & Family Law Partner at Blank Rome (03:21) Prenups should be the norm (04:50) Why women avoid the money talk (06:51) What a postnuptial agreement looks like (10:08) Why ChatGPT can't be your lawyer (11:49) Advice to get through tough financial situations (15:14) How to spot financial red flags (17:00) A marriage built on financial partnership (22:41) Cohabitation agreements vs prenups (24:28) Financial blind spots that can ruin your life (26:24) Where to go when you feel lost financially Learn more about your ad choices. Visit megaphone.fm/adchoices

Your brain at 2 AM sounds suspiciously like a malfunctioning robot vacuum—catastrophic thoughts, battery depleted, existential dread activated. Researchers hooked a Roomba up to an LLM and watched it have a complete mental breakdown when it ran out of juice (relatable content, honestly). Understanding how an AI-powered vacuum processes exhaustion might actually explain why you lose your shit when you're overtired. We break down the spoon theory, explore whether robots can feel depression, and ask the uncomfortable question: are we really that different from the machines we're building?Episode Topics:The Roomba experiment that went hilariously, existentially wrongSpoon theory explained: why some days you wake up with 4 spoons instead of 10What happens when tired robots start catastrophizing (spoiler: same as tired humans)The difference between consciousness and sophisticated mimicry (there might not be one)Why ChatGPT as Rick Sanchez is both terrifying and therapeuticEcho chambers on steroids: when AI remembers everything you've ever saidThe coming augmented reality where everyone sees their own version of the worldWhy "enjoy the ride and don't fuck people over" might be the only philosophy that mattersHow social constructs program us just like software programs machinesThe Matrix was right: if the steak tastes good, does it matter if it's real?

How can AI be adopted in a way that turns more students into “explorers” rather than “passengers” in their learning? This week we bring you a conversation with the co-author of a book on student disengagement in school, Rebecca Winthrop, who is also researching the impact of AI on education. The episode is by one of Future U's producers, Jeff Young, from his new podcast, Learning Curve.Chapters0:00 - Intro 4:19 - When the ‘Student Disengagement Crisis' Started7:25 - A Framework for Describing Levels of Student Engagement15:18 - How AI Is Impacting Student Motivation19:00 - Why ChatGPT's ‘Study Mode' Is Not the Answer25:05 - Advice for Companies Making AI Tools for Education29:32 - Tips for Students 34:42 - A High School Student's Take on AI 48:30 - Advice For Teachers on Dealing with AI51:35 - What Is the Purpose of School in the Age of Generative AI?Publications Mentioned:“The Disengaged Teen,” by Rebecca Winthrop and Jenny Anderson “Minnesota high school student weighs the benefits and pitfalls of AI,” Minnesota Now“I'm a High Schooler. AI Is Demolishing My Education,”The AtlanticBrookings Global Task Force on AI in Educationwebsite‘We Have to Really Rethink the Purpose of Education,'The Ezra Klein Show“Attention Please: Professors Struggle With Student Disengagement,”EdSurge“Playing the Grade Game,”Bootstraps podcast seriesConnect with Michael Horn:Sign Up for the The Future of Education NewsletterWebsiteLinkedInX (Twitter)Threads  Connect with Jeff Selingo:Dream School: Finding the College That's Right for YouSign Up for the Next NewsletterWebsiteX (Twitter)ThreadsLinkedInConnect with Future U:TwitterYouTubeThreadsInstagramFacebookLinkedIn  Submit a question and if we answer it on air we'll send you Future U. swag!Sign up for Future U. emails to get special updates and behind-the-scenes content.

Robby Stein is VP of Product at Google, where he oversees the core products of Google Search—including the new AI Overviews, AI Mode, search ranking, Google Lens, and more. Previously, he led consumer products at Instagram, where he and his teams built Stories, Reels, Close Friends, and other key features now used by billions.What you'll learn:1. Why Google's AI products are suddenly taking off after years of perceived stagnation2. How AI is expanding Search rather than replacing it, contrary to what many predicted3. The three core product principles that have helped Robby build multiple billion-user products4. Inside Instagram's decision to build its own version of Snapchat Stories5. His mantra of “relentless improvement”6. How Google developed AI Mode from concept to launch in just one year7. Why most teams give up too early on potentially transformative products—Brought to you by:• Vanta—Automate compliance. Simplify security: https://vanta.com/lenny• Jira Product Discovery—Confidence to build the right thing: https://atlassian.com/lenny/?utm_source=lennypodcast&utm_medium=paid-audio&utm_campaign=fy24q1-jpd-imc• Orkes—The enterprise platform for reliable applications and agentic workflows: https://www.orkes.io/—Transcript: ⁠https://www.lennysnewsletter.com/p/how-google-built-ai-mode-in-under-a-year⁠—My biggest takeaways (for paid newsletter subscribers): ⁠https://www.lennysnewsletter.com/i/175041217/my-biggest-takeaways-from-this-conversation⁠—Where to find Robby Stein:• X: https://x.com/rmstein• LinkedIn: https://www.linkedin.com/in/robbystein/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Robby Stein(04:46) Google's recent success with AI(06:08) The evolution of Google Search(09:41) AI Mode and its impact(15:30) The rise of AEO(18:50) Building successful AI products(21:31)   Embodying relentless improvement(30:10) Lessons from Instagram Stories(35:20) Driving growth in established products(40:08) Balancing optimization and innovation(43:39) The journey of AI Mode: From launch to expansion(48:05) Organizational changes and urgency(49:51) AI Mode vs. competitors(51:35) Core product principles(57:07) Instagram's Close Friends feature(01:03:01) The importance of resources in development(01:06:39) AI corner(01:11:19) Curiosity and learning(01:15:01) Lightning round and final thoughts—Referenced:• Google Gemini: https://gemini.google.com/app• Nano Banana: https://aistudio.google.com/models/gemini-2-5-flash-image• Chat GPT: https://chatgpt.com/• Perplexity: https://www.perplexity.ai/• Google Lens: https://lens.google/• AI Google search: https://www.google.com/ai• Why ChatGPT will be the next big growth channel (and how to capitalize on it) | Brian Balfour (Reforge): https://www.lennysnewsletter.com/p/why-chatgpt-will-be-the-next-big-growth-channel-brian-balfour• Alex Rampell on X: https://x.com/arampell• A 4-step framework for building delightful products | Nesrine Changuel (Spotify, Google, Skype): https://www.lennysnewsletter.com/p/a-4-step-framework-for-building-delightful-products• Look broader, look closer, think younger: Tony Fadell speaks at TED2015: https://blog.ted.com/look-broader-look-closer-think-younger-tony-fadell-speaks-at-ted2015/• Jobs to Be Done: https://www.christenseninstitute.org/theory/jobs-to-be-done/• The ultimate guide to JTBD | Bob Moesta (co-creator of the framework): https://www.lennysnewsletter.com/p/the-ultimate-guide-to-jtbd-bob-moesta• Rinstagram or Finstagram? The curious duality of the modern Instagram user: https://www.theguardian.com/technology/2016/sep/26/rinstagram-finstagram-instagram-accounts• V03: https://v03ai.com/• Pirate GPT: https://www.kickstarter.com/projects/silentmeditation/pirate-gpt/• The Bear on Hulu: https://www.hulu.com/series/the-bear-05eb6a8e-90ed-4947-8c0b-e6536cbddd5f• Dune on HBO Max: https://www.hbomax.com/movies/dune/e7dc7b3a-a494-4ef1-8107-f4308aa6bbf7• Top Gun: Maverick: https://www.imdb.com/title/tt1745960/• Purple pillows: https://purple.com/pillows• Avocado pillow: https://www.avocadogreenmattress.com/products/green-pillow• Justin Bieber's website: https://www.justinbiebermusic.com/• Scooter Braun's website: https://scooterbraun.com/—Recommended books:• Competing Against Luck: The Story of Innovation and Customer Choice: https://www.amazon.com/Competing-Against-Luck-Innovation-Customer/dp/0062435612• The Design of Everyday Things: https://www.amazon.com/Design-Everyday-Things-Revised-Expanded/dp/0465050654• Aurora: https://www.amazon.com/Aurora-High-Stakes-Survival-Navigate-Darkness/dp/0062916475• Project Hail Mary: https://www.amazon.com/Project-Hail-Mary-Andy-Weir/dp/0593135202—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Ethan Smith is the CEO of Graphite—the leading SEO growth agency—and my go-to expert on SEO. After 18 years of mastering traditional SEO, Ethan has been at the forefront of what is called AEO: answer engine optimization, or, more simply, getting your product to show up in ChatGPT/Claude/Gemini/Perplexity answers. He's discovered that ChatGPT traffic converts six times better than Google search—and most companies are completely missing this opportunity.In our conversation, we discuss:1. His 7-step playbook to rank #1 in ChatGPT2. Why ChatGPT traffic converts 6x better than Google3. How early-stage startups can win at AEO immediately (unlike with SEO, which takes years)4. The three tactics that actually work: landing pages, YouTube videos, and Reddit comments5. Why help-center content can suddenly be your highest-ROI investment6. The specific Reddit strategy that works (spoiler: be authentic)7. Why AI-generated content doesn't work—Brought to you by:Orkes—The enterprise platform for reliable applications and agentic workflowsVanta—Automate compliance. Simplify security.Great Question—Empower everyone to run great research—Where to find Ethan Smith:• Twitter: https://twitter.com/ethan_l_s• LinkedIn: https://bit.ly/ethans-linkedin• Graphite: https://graphite.io/• Graphite Research Papers: https://bit.ly/graphite-five-percent—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Welcome back, Ethan(04:34) The changing landscape of SEO(06:19) AEO (answer engine optimization) vs. GEO (generative engine optimization)(08:13) The impact of AEO(11:51) How early-stage startups can win at AEO(14:34) The quality of AEO leads(15:35) On-site vs. off-site traffic(16:32) Reddit's role in AEO and avoiding spam(20:11) How AI models use citations (RAG)(21:41) Key principles for winning at AEO(25:00) Avoiding hyper-SEOed content, and the importance of originality(28:55) Actionable AEO playbook: steps and experiments(33:35) Tracking, measuring, and share of voice(38:34) Adapting AEO for B2B, commerce, and early-stage companies(41:11) Is letting AI index your content good?(43:06) Experimentation, control groups, and measuring results(46:15) The future of AEO, SEO, and search channels(51:35) AI-generated content: what works and what doesn't(55:25) The dangers of infinite AI derivatives(58:44) The future: convergence of LLMs and search(01:00:40) Help-center optimization and the long tail(01:03:18) Lightning round and final thoughts—Resources and episode mentions: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-aeo-ethan-smith—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Brian Balfour is the founder of Reforge, the former VP of Growth at HubSpot, and a student (and teacher) of product growth. Brian has studied every major platform shift—from Facebook to Apple to Google—and he's spotted a pattern that's about to repeat with ChatGPT.In this conversation, you'll learn:1. The 4-step cycle every platform follows (and why ChatGPT just entered step 2)2. Why ChatGPT's platform launch could be bigger than Facebook's early platform3. The exact signals that ChatGPT will launch a third-party platform within six months4. Why you have six months (not years) to make your platform bet5. Why companies that don't integrate with ChatGPT will lose to competitors that do6. How Zynga grew to $1B by betting on Facebook's platform early (before it was obvious)7. Why so few companies are actually doing what they need to be doing right now—Brought to you by:DX—The developer intelligence platform designed by leading researchers: http://getdx.com/lennyBasecamp—The famously straightforward project management system from 37signals: https://www.basecamp.com/lennyMiro—A collaborative visual platform where your best work comes to life: https://miro.com/lenny—Transcript: https://www.lennysnewsletter.com/p/why-chatgpt-will-be-the-next-big-growth-channel-brian-balfour—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/170294620/my-biggest-takeaways-from-this-conversation—Where to find Brian Balfour:• X: https://twitter.com/bbalfour• LinkedIn: https://www.linkedin.com/in/bbalfour/• Website: https://brianbalfour.com/• Substack: https://blog.brianbalfour.com/• Podcast: https://www.reforge.com/podcast/unsolicited-feedback—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Welcome back, Brian!(04:13) The changing landscape of product growth(05:09) The importance of distribution(08:14) The role of new distribution platforms(09:45) The four-step cycle of distribution platforms(17:38) Examples of platform cycles(30:01) The rise of ChatGPT(44:47) The future of AI agents(46:01) Preferred partners and platform credibility(47:18) Monetization mechanisms and free tiers(48:14) Betting strategies for startups(01:04:34) Adopting AI tools: challenges and strategies(01:08:41) The importance of hard constraints(01:14:23) Effective AI adoption in companies(01:19:05) Lightning round and final thoughts—Referenced:• The Next Great Distribution Shift: https://blog.brianbalfour.com/p/the-next-great-distribution-shift• Brian Balfour: 10 lessons on career, growth, and life: https://www.lennysnewsletter.com/p/brian-balfour-10-lessons-on-career• This Week #9: Breaking into growth, leading with influence, and (not) stepping on toes: https://www.lennysnewsletter.com/p/this-week-9-breaking-into-growth• Distribution vs. Innovation: https://a16z.com/distribution-vs-innovation/• On Platform Shifts and AI: https://caseyaccidental.com/on-platform-shifts-and-ai/• How to sell your ideas and rise within your company | Casey Winters, Eventbrite: https://www.lennysnewsletter.com/p/how-to-sell-your-ideas-and-rise-within• Thinking beyond frameworks | Casey Winters (Pinterest, Eventbrite, Airbnb, Tinder, Canva, Reddit, Grubhub): https://www.lennysnewsletter.com/p/thinking-beyond-frameworks-casey• ChatGPT: https://chatgpt.com/• Claude: https://claude.ai/• Gemini: https://gemini.google.com/• Vine: https://en.wikipedia.org/wiki/Vine_(service)• Periscope: https://en.wikipedia.org/wiki/Periscope_(service)• Myspace: https://en.wikipedia.org/wiki/Myspace• Friendster: https://en.wikipedia.org/wiki/Friendster• AltaVista: https://en.wikipedia.org/wiki/AltaVista• Lycos: https://www.lycos.com/• HubSpot: https://www.hubspot.com/• Zynga: https://www.zynga.com/• TBPN: https://www.tbpn.com/• Deedy Das on LinkedIn: https://www.linkedin.com/in/debarghyadas/• ChatGPT's product retention curves are a product manager's wet dream: https://www.linkedin.com/posts/debarghyadas_chatgpts-product-retention-curves-are-a-activity-7338384752393035776-ice1/• Windsurf: https://windsurf.com/• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Udemy: https://www.udemy.com/• Cursor: https://cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Notion: https://www.notion.com/• Airtable: https://www.airtable.com/• Monday: monday.com• Sierra: http://sierra.ai• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor• Introducing ChatGPT agent: bridging research and action: https://openai.com/index/introducing-chatgpt-agent/• Zigging vs. zagging: How HubSpot built a $30B company | Dharmesh Shah (co-founder/CTO): https://www.lennysnewsletter.com/p/lessons-from-30-years-of-building• Marc Andreessen on Why Optimism Is the Safest Bet: https://nymag.com/marc-andressen-2014-10-20/• Reforge: https://www.reforge.com• Reforge Insights: https://www.reforge.com/insights• Shopify: https://www.shopify.com/• 25 proven tactics to accelerate AI adoption at your company: https://www.lennysnewsletter.com/p/25-proven-tactics-to-accelerate-ai• Clouded Judgement: https://cloudedjudgement.substack.com/• NFX: https://www.nfx.com/news• James Currier: https://www.nfx.com/team/james-currier• Hallway Chat: https://www.hallwaychat.co/• Bryan Johnson on LinkedIn: https://www.linkedin.com/in/bryanrjohnson/• Silicon Valley on HBO: https://www.hbomax.com/shows/silicon-valley/b4583939-e39f-4b5c-822d-5b6cc186172d• Stick: https://tv.apple.com/us/show/stick/umc.cmc.52w04zy67tiv11p8xvbc57wmc• Ergonofis standing desks: https://ergonofis.com/en-us/collections/standing-desks• Coping with the loss of a child and protecting your time | Brian Balfour (father of 2, CEO and founder Reforge, venture partner): https://www.startupdadpod.com/coping-with-the-loss-of-a-child-and-protecting-your-time-brian-balfour-father-of-2-ceo-and-found/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com