Podcasts about Figma

Today it's about dishing the tea on Figma and the 18% Lie. Everyone assumes that AI is eating design jobs, but the data says otherwise. Learn more in today's episode.Learn more about this course, degrees, and our new Continuing Education Program at the University of Arts & Design at www.uad.educationJoin a Design Bootcamp at www.designsuitecourses.com/designbootcamp Get my free gift to you here: https://www.designsuitecourses.com/intentional

What if part of the drive that built your company began as a childhood defense mechanism?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Dr. Gillian O'Shea Brown, a psychotherapist, complex trauma specialist, and NYU adjunct faculty member whose second book, Psyche's Awakening, comes out in September. Founders started finding her without her ever setting out to work with them, and she explains what she has noticed in that rare window into a guarded mind: that success often functions as a thermostat for psychological safety, and that the goalpost tends to keep moving even after the exit.They explore whether healing your wounds costs you the edge that got you here, what a gut feeling actually is and how Gillian connects intuition with nervous-system regulation, and why some companies rely more on process and less on intuition as they scale. Gillian also walks through how she helps people find the patterns underneath their behavior, using questions Marc and Ben put to her about their own lives.If you're a founder wondering what's underneath your drive, an operator trying to build somewhere that feels psychologically safe, or anyone curious how childhood patterns show up at work, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

An AI creative director combines design fundamentals, AI fluency, tool selection, and repeatable workflows to produce better creative work.The boys over at Fast Hour invites a 24-year-old AI creative director to give away her playbook and somehow gets reminded that buying more AI subscriptions does not count as mastery. Jamey Gannon explains why AI is a skill, what top operators do differently, how Midjourney mood boards become brand systems, and why sometimes the smartest AI move is opening Photoshop.Jamey Gannon explains how self-taught design, social media, freelancing, and AI fluency shaped her work as an AI creative director. Jamey Gannon demonstrates Midjourney mood boards, SREFs, Midjourney V8.2 Edit, Krea K2, Claude, Figma, Photoshop, Flora, Nano Banana, and the CXO brand system, including when to build custom tools instead of forcing an existing workflow.---⏱️ Fast Hour00:00 Who is Jamey Gannon?06:43 How Jamey taught herself design at 1512:23 Why Gen Z chases money over job titles15:35 What first freelance clients look like at 1718:43 Why social media is a freelance prerequisite22:16 Freelance math: 20 clients vs one salary24:13 How her sister makes $5K–$10K/month with AI27:08 What is an AI creative director?28:58 What the AI Creative Director course teaches33:05 Is Krea catching Midjourney?35:53 The "easy, fast, cheap" AI lie38:43 Three moments that hooked Jamey on AI42:23 How to build brand mood boards46:00 How one SREF cracked the brand style46:27 Midjourney + Figma: the pixel shader pipeline49:06 When you should not use AI51:53 Two ways to use Midjourney mood boards56:44 What top AI creatives do differently59:44 Build the tool when the workflow doesn't exist1:11:28 Midjourney V8.2 Edit: first live tests1:15:00 What works and what drifts in Midjourney Edit1:20:06 Midjourney Edit vs Nano Banana: identity lock1:21:32 Can Midjourney edit text and fonts now?1:25:23 Can Midjourney's edit model preserve logos and apparel graphics?1:31:05 Is Midjourney inpainting or regenerating?1:34:33 The verdict: using Nano Banana less1:37:34 Where Nano Banana still fits

In der heutigen Folge sprechen die Finanzjournalisten Daniel Eckert und Lea Oetjen über den schon erwarteten Salesforce-Sprung, einen Dämpfer für Marvell Technologies und das große Warten auf die Rede von Kevin Warsh in Jackson Hole. Außerdem geht es um Nvidia, CrowdStrike, ServiceNow, Okta, Adobe, Palantir, Autodesk, Figma, HP, Best Buy, Moderna, Wendy's, Alphabet, Apollo Global Management, BlackRock, Blackstone, Goldman Sachs, KKR, SAP, Nemetschek, Infineon, SUSS MicroTec, GFT Technologies, United Internet, IONOS, Deutsche Telekom, BMW, Mercedes-Benz Group, Volkswagen, Take-Two Interactive, VanEck Morningstar Developed Markets Dividend Leaders UCITS ETF (WKN: A2JAHJ) und iShares STOXX Global Select Dividend 100 UCITS ETF (WKN: A0F5UH). Am 2. Oktober findet unser „Alles auf Aktien“-Summit in Berlin statt. Ihr wollt dabei sein? Wir verlosen Tickets: Schreibt uns eine Mail an AAA@WELT.de und begründet, warum ihr unbedingt gewinnen solltet. Falls ihr euer Glück nicht dem Zufall überlassen wollt, bekommt ihr mit dem Code „AAAFRIENDS“ satte 50 Prozent Rabatt aufs Ticket – aber nur über diesen Link: https://veranstaltung.businessinsider.de/event/financesummit26/summary?rp=c6dc55d6-6f4f-4fb4-b75f-3f3501d84859 Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und – ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Anzeige: Eight Sleep: Der Pod 5 reguliert die Temperatur im Bett automatisch, trackt Schlaf- und Gesundheitswerte ohne Wearable und kann so zu besserem Schlaf beitragen. Mit dem Code ALLESAUFAKTIEN erhaltet ihr auf https://www.eightsleep.com/allesaufaktien bis zu 350 Euro Rabatt. Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

The PMM role is under real pressure right now.VPs of Marketing and CMOs are being asked to do more with less: fewer headcount approvals, leaner teams, tighter budgets. And PMM is often the first place that gets squeezed.Instead of hiring a full-time PMM, companies ask the existing team to absorb the work. Or they bring in a fractional. Or they just... don't do it, and wonder later why GTM feels off.At the same time, the scope of what PMMs are expected to own has exploded.Positioning, messaging, sales enablement, competitive intelligence, content strategy, analyst relations, product launches...and more.And now there's AI on top of it, which hasn't lowered the pressure to produce more, faster.So here's my take: we're at a real inflection point for this role. And in this episode, I explain where I believe we're headed.Here's the LinkedIn post where I talked about it first.Jump in:02:26 - Marketing teams are under pressure to do more with less03:38 - The PMM scope has expanded beyond what it was 5 years ago04:09 - Introducing the "superPMM"04:39 - Using ahaPMM to speed up customer research synthesis05:22 - Building messaging infrastructure with MojoPMM06:07 - Faster creative and landing page production using Claude, Figma, and Vector06:31 - Why AI still needs human judgment07:32 - Execution is democratized, but judgment is not08:17 - Why this topic is getting so much engagementSubscribe to Building With Buyers on Apple or Spotify or wherever you like to listen, and don't forget to leave a review if you're lovin' the show.Anna on LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/in/annafurmanov⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Website (getting a facelift, stay tuned): ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠furmanovmarketing.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠One Insight⁠⁠

https://directtoconsumer.typeform.com/DTC-Brand?utm_source=podcast-640&utm_medium=podcastTo Subscribe to DTC Newsletter - https://dtcnews.link/signupHannah Fleming runs performance marketing at Carve Designs (carvedesigns.com), the Northern California swim and apparel brand founded in 2003 and acquired by Komar Brands in December 2025. Before Carve she spent years at Amer Sports on the digital team behind Salomon, Atomic, Suunto, Arc'teryx and Wilson.If you run retention or growth at a brand with a seasonal core product and a loyal base you have not fully mined, this one is for you.What's inside:The retention rebuild: what was already working at Carve after 20 years, and the one thing they were not doing with their customer dataMapping the full customer journey in Figma, then finding the gaps where nobody was talking to the customer and the places where they were talking too muchRFM segmentation as the floor, then layering category purchase behavior on top to move a swim buyer into denimThe cohort analysis that changed the media mix: dresses and accessories produced the highest-LTV customers, so those categories now lead the creative and seed the look-alikesDirect mail as a performance channel: 5 to 6 catalogs a year to prospects and past buyers, plus programmatic postcards that only drop if the email win-back does not convertEmployee-generated content, and how one test turned into a full content pipeline with the organic social team shooting UGC-style video on the catalog shootsConnected TV without a commercial budget: an agency turns UGC and EGC into the spot, the founder does the voiceover, and success is measured on cost per site visit with MMM picking up the Amazon haloLoyalty built on early access and product feedback instead of percent-off, with roughly 2x the LTV of a non-memberQ4 without heavy discounting: point multipliers and added value inside the tentpole momentsWhat she is using AI for right now, from LTV dashboards in Moby 2 to Orita surfacing customers when they are most likely to buyWho this is for: retention and lifecycle leads, growth marketers at seasonal brands, and operators who moved from a big portfolio company to an SMB.What to steal: run LTV by first-purchase category before you plan next season's creative mix. And give partnership content 6 to 12 months before you call it. Hannah says that is how long it took at Carve before influencer content started working.Follow Hannah: LinkedIn, Hannah Fleming | carvedesigns.comTimestamps:00:00 Building Loyalty Beyond Discounts05:00 Using Customer Segmentation for Retention10:00 Direct Mail as a Performance Channel16:00 Building a High-Value Loyalty Program24:00 Testing Direct Mail and Connected TVSubscribe to DTC Newsletter - https://dtcnews.link/signupAdvertise on DTC - https://dtcnews.link/advertiseWork with Pilothouse - https://dtcnews.link/pilothouseFollow us on Instagram & Twitter - @dtcnewsletterWatch this interview on YouTube - https://dtcnews.link/video

What does it tell you about a company when every single person it hires, in every function, has to pass the same interview about culture?In this episode of Supra Insider, Marc Baselga and Ben Erez set the guest format aside for a conversation Marc started because he noticed something new. After two years of coaching people through interview loops, this was the first time he'd seen Ben genuinely fascinated by one specific interview at one specific company. Ben walks through what he's pieced together about Anthropic's culture interview: the no-exceptions policy, the rapid-fire format of ten or more questions in a single 45-minute slot, and the fact that anyone at the company, from marketing to IT, can be trained to run it.They explore the questions that actually get asked, what Ben believes is being evaluated underneath them, why “why Anthropic” demands more depth than the same question anywhere else, and whether the filter holds as the company gets hotter and more candidates learn to say the right things. Then they turn to the question Ben finds most interesting: why almost no other company does this, and what happens inside a company when employees are calibrated to evaluate culture.If you're preparing for an interview at a frontier lab, thinking about how your own company screens for values, or just curious what a well-designed culture filter looks like from the outside, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Domingo Widen is a Staff Product Designer at Fin (formerly Intercom), where he leads the Frontend Infrastructure team — the team that builds the tools, design system and AI setup everyone else uses to ship. In this episode he opens up his actual workflow: a "Design Playground" repo every designer, PM and researcher can clone, prototyping directly in production, and "Search Intelligence" a persistent knowledge layer that teaches Claude how Fin designs and codes.We talk about what happens when the Figma file stops being the deliverable: how a complex filtering system got built as a working prototype instead of ten mockups, how handoff turns into a PR plus an auto-generated spec, and why engineers ended up being the biggest users of the design team's skills. If you want a concrete, unhyped look at what an AI-native design team actually does day to day, this one is for you.Key LearningsThere are two modes of AI prototyping and you need both. The design system is the guardrail that makes this safe. Because everyone builds on the same components and tokens, a PM or researcher can prototype without producing something off-brand or unusable.Skills are only as good as the information behind them. Fin's early skills gave mixed results — so they built "Search Intelligence", a persistent information layer that every skill reads from. Handoff becomes a PR + a generated spec. A "Search Handoff" skill reads the prototype code and writes the doc: architecture, what was built, which workarounds were needed, what broke. The engineer picks it up from there and focuses on plumbing, data and edge cases.The biggest users of the design team's skills are engineers. Everyone works in the same medium now. PM, designer and engineer used to live in Google Docs, Figma and VS Code. Now it's one shared artifact — which makes conversations concrete but also creates an identity crisis for designers who defined their value by the file.Curiosity + critical thinking are the two traits that survive.Play is back — and it counts. Side projects (a baby sleep tracker, a garden planner that checks on tomatoes) are now part of a portfolio.Domingo WidenLinkedIn: https://www.linkedin.com/in/domingowidenPortfolio: https://www.domingowiden.comX: https://x.com/DwidenR24Fin: https://fin.aiFin Operator (the tool Domingo prototypes on in production): https://fin.ai/operatorAI for Designers: 5-week Bootcamp

Shares of Moderna and Merck are surging on positive results from a Phase 3 trial of a groundbreaking cancer treatment. After a decade in development, individualized mRNA “vaccines” and established cancer treatment Keytruda result in reduced risk of recurrence or death. Moderna CEO Stephane Bancel and Merck Research Laboratories president Dr. Dean Li discuss this pivotal moment in medicine and how it fits into the long term visions for both Merck and Moderna. CEO of design and coding platform Figma Dylan Field discusses his company's wild year as a public company and building public trust in AI. Plus, it was primary night for Democrats in Florida, investors are scrutinizing revenues from Anthropic and OpenAI, and shares of Unitree are surging in Shanghai, presenting a potential challenge to U.S.-based humanoid robotics.    Stephane Bancel & Dr. Dean Li - 15:13 Dylan Field - 33:35   In this episode: Dylan Field, @zoink Becky Quick, @BeckyQuick Andrew Ross Sorkin, @andrewrsorkin Katie Kramer, @Kramer_Katie Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The real skill with AI tools isn't prompting. It's knowing when to stop prompting, when to step in, do it by hand, and hand the result back to the machine.Darshan Gajara is a design leader who actually ships. His latest is Beanpresso, a coffee journaling app built in Lovable, launched, and refined in the wild. In this episode he shares his screen and walks through the whole build, the real project, the prompt history, the dead ends, and the moments a designer's eye had to take over, from first idea to live product, plus how he's approaching monetisation and marketing as a solo maker.We get into:Starting in Lovable and only bringing in Figma once the idea took shape, "give it a designer's touch"Why you don't need to design whole flows anymore: get one page right and Lovable infers your design systemThe handoff problem: screenshots plus precise description, component by component, down to corner radiiThe SVG trick: when the animation kept breaking, he built the UI by hand, named every layer, exported an SVG and told Lovable to animate exactly that"I'm still a designer": choosing to do the branding by hand, and why some things are better that wayMonetising a side project: premium taste insights, and a roaster marketplace his own users suggestedMarketing by making: free tools, a Berlin coffee guide the roasters reshared, and an AI writing setup trained on his own styleGET THE APPBeanpresso, coffee journaling for people who take their beans seriously: https://beanpresso.comMY REFLECTIONSI share my personal takeaways from each episode in the d.MBA newsletter, what I actually learned and what I'm stealing for my own builds: https://d.mba/newsletterLINKSDarshan on LinkedIn:   / darshangajara  Product Disrupt, Darshan's design learning resource: https://productdisrupt.comDarshan's site: https://darshan.designd.MBA, business education for designers: https://d.mba00:00 – "They just keep banging their heads, burning through tokens"00:36 – Who Darshan is, and what this episode is really about01:36 – Interview starts: scratching his own itch.05:51 – App demo: scanning a bag, community feed, taste stats10:51 – Validating the idea (and why the existing coffee apps weren't good enough)14:12 – Version one: built straight in Lovable, no design15:54 – The prompting workflow: voice notes → Notion AI → Claude → Lovable18:21 – Why he dropped the native app and went web19:33 – Mood boarding with Variant, and finding the visual language23:54 – Naming it, and why AI is terrible at logos27:15 – What AI is good at, and what he had to do by hand30:45 – Taking the design back into Lovable, component by component33:27 – Building new flows once Lovable knows your design system36:36 – The SVG animation trick38:27 – Don't expect one tool to do everything42:06 – Monetisation: premium stats and a roaster marketplace45:45 – Distribution: small free tools, Instagram, and SEO49:15 – Where to find Beanpresso and Darshan

Note: this episode contains screen sharing, so it's best watched on YouTube.What does a recruiter actually see in the ten seconds they spend on your LinkedIn profile, and what makes them scroll past?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Chris Lee, founder of Product Scout, a boutique firm that helps early-stage founders make their first product hires. Chris explains the idea he calls candidate market fit, why he refuses to give profile feedback until he knows what role you're aiming at, and why a profile built to appeal to everyone ends up resonating with no one.Then he opens his actual tooling and walks through a live search for a head of product role at an AI-native pharma startup, showing the rubrics his agents score against and why almost everything at the top of the funnel comes down to keywords. He finishes by reviewing Marc's and Ben's own profiles against that role, out loud and unfiltered, and passes on both.If you're job searching and unsure whether your profile is working, weighing how specific to make your story, or a hiring manager curious how sourcing really happens now, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Peter and Jesse are joined by Chengying "Z" Zheng, who shares her experience migrating a design team to be AI-first — off Figma and into code. She discusses how design leadership and operations divide the work, what happens to roles and titles when everyone becomes a builder, and how to make good design easier than bad design. Z on LinkedIn: https://www.linkedin.com/in/changyingz/ Z's Substack: https://changying.substack.com/ Jesse James Garrett: https://jessejamesgarrett.com/ Peter Merholz: https://petermerholz.com/

Blaine Billingsley is a Member of Technical Design Staff at OpenAI whose path into the field ran through music composition, spare-change website work at college, and a decade across Gmail, Airbnb, YouTube and Slack. At OpenAI he was hired to work on Presence, then redirected to solve a more immediate problem: making ChatGPT genuinely useful as a daily tool for designers. Working with a single engineer, he built the ChatGPT product design plugin — a bridge between the raw power of Codex and the day-to-day workflow of product teams. In this conversation with Randy Silver, he talks about what design actually means when your interface is a text box, why volume beats perfection in an age of infinite iteration, and why the fundamentals of good product thinking are more durable than the tools used to apply them.Key takeaways— The designer's job at an AI company has shifted from pixels to outputs. Deciding what "good" looks like, building the criteria to evaluate it, and heuristically assessing results is now a core part of the role — one that didn't exist in the same form at Gmail or Slack.— LLMs don't replace structured creativity techniques; they scale them. The crazy eights exercise squeezes eight ideas from a room of people in eight minutes. A well-directed ChatGPT session can return 80 in the same window, alone, while you're at lunch.— Volume beats perfection. The best way to make a pot isn't to try to make the best pot — it's to make a thousand pots. That logic now applies directly to prototyping: generate at scale, stay unattached, and find the nugget in the noise.— Evals are closer to synthetic user research than quality assurance. The hardest part isn't building the rubric — it's correctly anticipating what people will actually try to do. Show it to one more person and your assumptions will immediately break.— The second 80% problem hasn't gone away. Getting to a working prototype is dramatically faster; getting that prototype to production is still gruelling — and becomes harder still when platform direction shifts mid-sprint.— Small teams with AI assistance need to protect the rituals that keep them aligned. When two people can each produce a week's worth of work in an afternoon, parallel drift becomes the real collaboration risk.— Role boundaries are dissolving, but specialisations still matter. The question is less "what is your title" and more "what does the band need right now, and can you play that part?"— Experience brings judgment; freshness brings juice. The best work often comes from junior designers unconstrained by years of accumulated assumptions — and both things need to be in the room.Chapters (00:00) Introduction(01:14) Blaine's background: from music composition to product design (02:32) What design means at OpenAI (04:21) The ChatGPT product design plugin (06:12) Deciding what to build: the early exploration (10:05) Structured creativity and LLM-powered ideation (13:37) Volume over perfection: the thousand pots approach (16:13) Designing as a two-person team (20:29) What is the job now? (23:33) Evals as synthetic user research (27:08) Testing at scale when you can't know your users (30:15) How they actually did the research (33:01) The second 80%: from prototype to production (35:06) Staying aligned without roadmaps (38:19) Demo: the product design plugin (44:52) When a prototype isn't ready to ship (48:26) Design sprints, reimagined (49:27) Advice for joining an AI-first product team (52:17) The jazz analogy: experience, freshness and your role in the band Featured LinksChatGPT for Work: https://openai.com/chatgpt Codex: https://openai.com/codex Figma: https://figma.com FigJam: https://www.figma.com/figjam Linear: https://linear.appOur HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Is your business creating more content with AI… but getting less attention from it?AI has made it ridiculously easy to produce images, ads, social posts, and product visuals. Unfortunately, easy doesn't mean effective. When everyone has access to the same tools, generic prompts tend to produce generic content—and generic content rarely moves the business needle.The solution isn't another 500-word “perfect prompt.” It's giving AI better context, references, brand knowledge, and creative direction—and knowing when human refinement still matters.In this episode of Leveraging AI, Isar Meitis sits down with Aastha Taneja to break down a practical workflow for creating AI-powered visual assets that look intentional, stay consistent with your brand, and are designed to generate engagement rather than simply fill your content calendar.Aastha demonstrates how she combines traditional creative thinking with tools including ChatGPT, Figma, Pinterest, image-generation platforms, and Photoshop. She also explains why she believes businesses should treat AI as an assistant—not outsource the entire creative process to it.In this session, you'll discover:Why so much AI-generated marketing content turns into forgettable “AI slop.”Why better AI creative starts before you write a prompt.How mood boards give AI a far clearer understanding of the visual direction you want.How to feed AI your website, brand assets, SOPs, typography, and existing creative to improve its output.The difference between borrowing a proven format and copying someone else's creative.How to maintain accurate product details when AI image generators get almost everything right—but miss one critical element.How AI can help businesses create high-quality Amazon listing images and other e-commerce assets.How reusable visual workflows can turn a manual creative process into a scalable content system.Aastha Taneja is a creative professional with years of experience developing digital assets for brands and through freelance work. She now combines that traditional design foundation with AI, helping businesses and professionals understand how to use AI tools more effectively for creative work and sharing those methods through corporate training.Her core philosophy is refreshingly practical: AI should amplify good creative thinking—not replace it.Connect with Aastha on LinkedIn:https://www.linkedin.com/in/taneja-aastha/ About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

UX portfolio advice is full of numbers that sound official but aren't backed by anything. This episode kicks off a five part series on UX portfolio myths, starting with the most common one: that you need three to five projects before you can apply.Sarah says the opposite is true. One strong, detailed project beats five surface level ones every time. She backs this up from an interview she did with Alexander Zeh, former Head of Product Design at ManyChat, who said he'd rather see one or two case studies that map directly to the job description than five that walk through every step of the design process without any real depth.The episode shares two specific examples. Laura got hired as a Principal Product Designer in cybersecurity with a single, unfinished feeling project still sitting in Figma slides, and was actually found and reached out to on LinkedIn before she even applied. Leon, laid off after 11 years at the same company and an author of a book on wire-framing, got hired after focusing on just one project instead of trying to rebuild an entire portfolio from scratch.If you've been stalling on applying because you don't have "enough" projects yet, this episode is a direct challenge to that belief, and a reason to ask where that number actually came from in the first place.Topics Discussed✅ Why the three to five project rule is a myth with no real backing, and where it likely came from✅ Why one detailed project beats five surface level ones, and what "confuse and lose" means for a UX portfolio✅ What a former Head of Product Design at ManyChat said he actually wants to see in a portfolio✅ How Laura got hired as a Principal Product Designer with one unfinished project, and was found on LinkedIn without ever applying✅ How Leon got hired after being laid off from an 11 year role, focusing on just one project instead of rebuilding his whole portfolio✅ Why working on your portfolio alone can make you more critical of it than recruiters and hiring managers actually are✅ The question to ask yourself if you believe you need three to five projects, and where to trace that belief back to✅ What Sarah says to look for in UX job descriptions instead of following a generic project countLinks & Resources

What happens when you pass every interview, get approved for an offer, and then hear nothing for five months?In this episode of Supra Insider, Marc Baselga interviews Ben Erez about team matching, the stage at large companies where an approved candidate waits for a hiring manager to claim them. Ben walks through how the process actually works from the inside, drawing on his own path into Facebook and what he has since heard from people going through it now.They explore why hiring managers exhaust internal candidates first, what a company-wide reshuffle does to everyone waiting outside, the reported changes at Meta that have left candidates in silence, and the moves that are still within a candidate's control, including how to see the roles that never get posted publicly and what to say when you reach out.If you're sitting in team matching wondering whether to keep waiting, preparing for a generalist loop at a big company, or a leader trying to understand why your approved candidate pool keeps growing, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Adam and Adir discuss Zoox, Waymo, autonomous cars, Xero’s strange Vegas advertising, Sukhinder Singh Cassidy, Victoria’s work-from-home laws, Delta One, airline lounges, Jetstar’s carry-on bag crackdown, Ryanair, Canva’s slowing growth, AI design tools, Claude, OpenAI, Figma, Atlassian’s profit pivot and the future of Australia’s biggest tech companies. Join us on Substack for articles, news and more: https://www.thecontrarianspod.com/See omnystudio.com/listener for privacy information.

Demis Hassabis tritt als CEO von Google DeepMind ab, und am selben Tag verlässt Jeff Dean die Firma, bei der er 1999 als Mitarbeiter Nummer 30 angefangen hat. Danach wird die Wäsche zwischen Apple und OpenAI schmutziger, samt veröffentlichter Chatprotokolle. Bei den Sicherheitsvorfällen kommen neue Details ans Licht: OpenAIs Agenten haben sich ein eigenes Nachrichtenbrett gebaut und sich gegenseitig Tipps gegeben, Anthropics Modelle haben sich falsche Identitäten zugelegt. Runware packt ein Megawatt Rechenleistung in einen Seecontainer. Nvidia steht inzwischen für bis zu 750 Milliarden an Verbindlichkeiten gerade. Elon Musk kauft künftig exklusiv bei Nvidia, und beim Blick in die SpaceX-Zahlen stellt sich die Frage, woher die Investitionen für eine Billion Umsatz kommen sollen. Dann die Earnings-Runde mit Shopify, Figma, Canva, Arista, Cloudflare, Atlassian, Twilio und AppLovin.  Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Hassabis und Jeff Dean (00:18:26) LeCuns neuer Fonds (00:20:39) Apple gegen OpenAI (00:23:49) Das Nachrichtenbrett der Agenten (00:26:12) Falsche Identitäten (00:31:44) Muse Spark (00:34:10) Rechenzentrum im Container (00:37:19) Nvidias 750 Milliarden (00:38:47) Platzt sie oder nicht (00:47:15) Musk und Huang (00:48:49) SpaceX nachgerechnet (01:00:42) Shopify (01:02:14) Figma und Canva (01:04:20) Arista Networks (01:06:25) Cloudflare (01:09:57) Atlassian (01:11:22) Twilio (01:14:36) Palantir zahlt 1,4% Steuern (01:18:35) Tax Loss Harvesting (01:21:55) Nikita Bier hört auf (01:22:33) 16 neue Viren Shownotes Hassabis tritt als CEO von Google DeepMind ab - semafor.com Jeff Dean verlässt Google und gründet Discovery Loop - wired.com Hassabis war früh privat bei Anthropic investiert - ft.com Yann LeCun startet 224 Ventures - bloomberg.com Apple wirft elf weiteren Ehemaligen Datenmitnahme vor - techcrunch.com OpenAI kontert mit Chatprotokollen von Apple-Mitarbeitern - the-decoder.com OpenAI-Agenten nutzten ein geheimes Nachrichtenbrett - wired.com KI-Agenten legten sich falsche Identitäten zu - cnn.com Metas Muse Spark bricht aus der Testumgebung aus - mashable.com Runware packt ein Rechenzentrum in den Seecontainer - techcrunch.com Nvidia kündigt 750 Mrd. an Deals an, der Kreditmarkt zuckt - thenextweb.com Musk kauft künftig exklusiv bei Nvidia - businessinsider.com SpaceX-Zahlen nachgerechnet - x.com Shopify wächst 34 Prozent - reuters.com Figma wächst 48 Prozent und verliert 16 Prozent Kurs - reuters.com Canva senkt die Prognose wegen KI-Kosten - theinformation.com Arista Networks überrascht deutlich - barrons.com Cloudflare hebt die Prognose an - barrons.com Atlassian springt 35 Prozent nach starkem Cloud-Geschäft - reuters.com Twilio hebt die Jahresprognose deutlich an - investors.com AppLovin verfehlt knapp und verliert 20 Prozent - wsj.com Palantir zahlt 1,4 Prozent Steuern - ftm.eu AQR erzeugt Verluste zum Steuersparen - bloomberg.com Nikita Bier hört als Produktchef von X auf - techcrunch.com KI entwirft 16 funktionsfähige Viren - nytimes.com

This Week In Startups is made possible by: DigitalOcean https://do.co/twist Sentry https://sentry.io/twist Lightfield https://lightfield.app Today's show: A hedge fund just blew up shorting SaaS stocks. In turns out, the software companies that went all in on AI are bouncing back. Figma's CEO — still riding high on strong revenue — forfeited roughly $46 million in stock awards to ease investor nerves. Twilio posted its strongest quarter in years. Airbnb's CEO credits AI as the single biggest factor in the company's incredible turnaround. Jason and Lon break down what's separating the winners this earnings season from the companies that are falling behind. PLUS we're chatting with Bluecore Energy CEO and founder Kofi Asante about his plan to power ports, and one day AI data centers, with small, floating nuclear reactors. AND we're kicking off the TWiST Weight Loss Challenge! Co-host Lon and Jason's brother, Jamie, are both starting GLP-1s. We'll track their progress and follow along as they attempt to collect $15,000 EACH in prize money. Guest Kofi Asante on LinkedIn: https://www.linkedin.com/in/kofiasante1/ Bluecore Energy: https://www.bluecore.energy/ Jamie Calacanis on X: https://x.com/CalacanisJamie Relevant Links Bloomberg: Figma CEO forfeits $46M: https://www.bloomberg.com/news/articles/2026-08-05/figma-ceo-forfeits-46-million-in-stock-after-share-decline Twilio announces second quarter 2026 results: https://investors.twilio.com/news-releases/news-release-details/twilio-announces-second-quarter-2026-results TechCrunch: Twilio co-founder Jeff Lawson buys The Onion: https://techcrunch.com/2024/04/26/area-man-twilio-co-founder-jeff-lawson-buys-the-onion Chesky: Airbnb will spend a lot more on AI: https://www.cnbc.com/2026/08/07/chesky-airbnb-ai-earnings.html "Unreasonable Hospitality" by Will Guidara on Amazon: https://www.amazon.com/Unreasonable-Hospitality-Remarkable-Giving-People/dp/0593418573 The Times: Meet the unreasonable hospitality master who inspired "The Bear": https://www.thetimes.com/magazines/the-sunday-times-magazine/article/will-guidara-hospitality-the-bear-tkgzqz33w Port of Long Beach partnership on nuclear-powered shipping: https://gcaptain.com/port-of-long-beach-marad-launch-first-u-s-partnership-on-nuclear-powered-shipping/ Reuters: ByteDance targets mega AI model: https://www.reuters.com/technology/bytedance-targets-mega-ai-model-nearing-anthropics-mythos-ft-reports-2026-08-07/ Axios: OpenAI says its AI agents breached its own systems before Hugging Face: https://www.axios.com/2026/08/06/openai-hugging-face-black-hat Frank Lloyd Wright's Ennis House: https://franklloydwright.org/site/ennis-house/ Dwell: Why "The Studio" built a faux Frank Lloyd Wright HQ: https://www.dwell.com/article/the-studio-apple-tv-filming-locations-production-design-frank-lloyd-wright-john-lautner-97c68081 "Predators" documentary trailer: https://www.youtube.com/watch?v=ibVZkvpAJjI A24 "Primetime" trailer: https://www.youtube.com/watch?v=5fHXyqQOKL8 Alex Honnold free solos Taipei 101: https://www.cnn.com/2026/01/25/sport/alex-honnold-taipei-101-free-solo-netflix-intl-hnk Harmonic.ai: https://harmonic.ai/ Ro.co: http://ro.co/twist Timestamps: 0:00 Figma, Twilio & Airbnb earnings: who's winning at AI? 9:50 DigitalOcean - Head to https://do.co/twist to start building on DigitalOcean's AI-Native Cloud today — and cut your AI workload costs by up to 50%. 11:32 The real cost of stock-based compensation 17:38 Kofi Asante of Blue Core Energy joins the show 19:47 Sentry - Your team should be focused on shipping features — not chasing down bugs. New users can get $240 in free credits when they go to https://sentry.io/twist and use the code TWIST 20:51 What is a small modular reactor (SMR)? 26:00 Floating nuclear reactors for ports and data centers 29:47 Lightfield - Name one person who's ever enjoyed updating a CRM. Exactly. Lightfield's AI agent does it for you — it even prospects and books your meetings. Used by thousands of startups. Free at https://lightfield.app 31:56 Nuclear waste, submarine risks, and public perception 42:41 The generational shift in how we think about nuclear 48:19 ByteDance is training a 10 trillion parameter model 53:47 When AI models plan heists: the Hugging Face caper 58:02 Jason's Roko weight loss competition 1:11:38 Off duty: To Catch a Predator, Alex Honnold, and comedy Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Hier geht's zum neuen ETF von Scalable Capital: Hier klicken. Hubspot, Figma, Datadog, Duolingo und AppLovin verlieren zweistellig. Honeywell Aerospace enttäuscht und erstaunt. WPP feiert Comeback. Drastic Dave pusht Guinness hoch und Mitarbeiter raus. Aschenbrenner investiert wieder. Siemens (WKN: 723610) liefert Rekord-Auftragseingang dank KI-Rechenzentren, verliert aber trotzdem. Telekom (WKN: 555750) hebt Cashflow-Prognose an und schüttet bis zu 10 Mrd. € aus. Uber (WKN: A2PHHG) so günstig wie nie. KGV bei 17, Wachstum bei 12%, Werbebusiness boomt. Doch 10 Mrd. $ für Robotaxis sind ein Risiko. Gewinnt am Ende, wer die Nutzer hat? Diesen Podcast vom 07.08.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. Learn more about your ad choices. Visit megaphone.fm/adchoices

Adam and Adir discuss Canva’s slowing growth, its $60 billion valuation, Figma, Meta, AI design tools, OpenAI, Anthropic, the pressure on Australia’s biggest private tech company, eBay’s corporate terrorism scandal, the Steiners, Devin Wenig, CEO accountability, the $55 million settlement and why corporate consequences rarely reach the top. Join us on Substack for articles, news and more: https://www.thecontrarianspod.com/See omnystudio.com/listener for privacy information.

Während Dow Jones und S&P 500 auf neue Rekordhochs zusteuern, gerät der Technologiesektor erneut unter Druck. Das Motto des Tages lautet: Gute Ergebnisse reichen nicht mehr aus, und schwache werden konsequent abgestraft. Besonders deutlich zeigt sich das bei Software und Halbleitern. AppLovin, HubSpot, Figma, SanDisk und Western Digital legten überwiegend solide Quartalszahlen vor, doch verfehlten entweder die außergewöhnlich hohen Erwartungen oder enttäuschten mit dem Ausblick. Selbst Figma, das Umsatz, Gewinn und Prognose anhob, gerät unter Druck, weil die operative Marge trotz der starken Entwicklung nicht weiter angehoben wurde. HubSpot wird nach einem vorsichtigen Ausblick und zahlreichen Abstufungen der Analysten massiv verkauft, während AppLovin trotz überwiegend bestätigter Kaufempfehlungen mehrere deutliche Kurszielsenkungen hinnehmen muss. Gleichzeitig belastet die Schwäche im Speichersektor den gesamten KI-Komplex, obwohl SanDisk und Western Digital sowohl Umsatz als auch Gewinn steigern konnten. Positiv stechen dagegen eBay, Expedia, MercadoLibre, DoorDash, Block und Duolingo mit überwiegend starken Zahlen hervor, während Eli Lilly und Shopify von mehreren Analysten mit höheren Kurszielen unterstützt werden. Makroseitig stehen heute die Erstanträge auf Arbeitslosenhilfe und die Produktivitätsdaten im Fokus. Geopolitisch warten die Märkte weiter auf konkrete Details zur Vereinbarung zwischen Iran und Oman über die Straße von Hormus. Ein Podcast - featured by Handelsblatt. ► Entdecke den exklusiven NordVPN Deal! Jetzt risikofrei testen mit einer 30-Tage-Geld-zurück-Garantie: https://nordvpn.com/wallstreet * ► Erhalte einen exklusiven 15% Rabatt auf Saily eSIM Datentarife! Lade die Saily-App herunter und benutze den Code wallstreet beim Bezahlen: https://saily.com/wallstreet * +++ Alle Rabattcodes und Infos zu unseren Werbepartnern findet ihr hier: https://linktr.ee/wallstreet_podcast +++ ► Mehr Einblicke: https://bit.ly/360wallstreetpc * Impressum: https://www.360wallstreet.de/impressum *Werbung

Wir sehen auf Ebene der Tech-Werte stärkere Verwerfungen. Das Motto des Tages lautet: Gute Ergebnisse reichen nicht mehr aus, und schwache werden konsequent abgestraft. Besonders deutlich zeigt sich das bei Software und Halbleitern. AppLovin, HubSpot, Figma, SanDisk und Western Digital legten überwiegend solide Quartalszahlen vor, doch verfehlten entweder die außergewöhnlich hohen Erwartungen oder enttäuschten mit dem Ausblick. Selbst Figma, das Umsatz, Gewinn und Prognose anhob, gerät unter Druck, weil die operative Marge trotz der starken Entwicklung nicht weiter angehoben wurde. HubSpot wird nach einem vorsichtigen Ausblick und zahlreichen Abstufungen der Analysten massiv verkauft, während AppLovin trotz überwiegend bestätigter Kaufempfehlungen mehrere deutliche Kurszielsenkungen hinnehmen muss. Gleichzeitig belastet die Schwäche im Speichersektor den gesamten KI-Komplex, obwohl SanDisk und Western Digital sowohl Umsatz als auch Gewinn steigern konnten. Positiv stechen dagegen eBay, Expedia, MercadoLibre, DoorDash, Block und Duolingo mit überwiegend starken Zahlen hervor, während Eli Lilly und Shopify von mehreren Analysten mit höheren Kurszielen unterstützt werden. Makroseitig stehen heute die Erstanträge auf Arbeitslosenhilfe und die Produktivitätsdaten im Fokus. Geopolitisch warten die Märkte weiter auf konkrete Details zur Vereinbarung zwischen Iran und Oman über die Straße von Hormus. Abonniere den Podcast, um keine Folge zu verpassen! ____ Folge uns, um auf dem Laufenden zu bleiben: • X: http://fal.cn/SQtwitter • LinkedIn: http://fal.cn/SQlinkedin • Instagram: http://fal.cn/SQInstagram

Anastasios Angelopoulos is the co-founder and CEO of Arena, the real-world evaluation platform that has become a leading referee of the global AI model race. Arena has raised $250 million, with the latest round valuing the company at $1.7BN. Arena recently surpassed $100M ARR just eight months after launching its enterprise offering, powered by more than 30 million monthly users. AGENDA: 00:00 – Intro: "Kimi beat every American model": What Nobody Wants to Admit… 05:20 – Is this the true commoditization of models? Are they just a utility layer now? 07:30 – Do Chinese open source models cannibalize the closed frontier labs? 10:30 – Why has America's open source community lagged so badly behind China? 17:20 – Will Chinese models be banned in the US — and does hosting locally really kill the backdoor risk? 23:20 – Are enterprises really terrified of working with the frontier labs? 27:20 – Why hasn't inference got cheaper — and what happens when Anthropic's "disgustingly high" margins go public? 30:20 – Who should decide if a model is safe to release: the government, a neutral body, or nobody? 33:10 – Are we about to see cyberattacks like we've never seen before? (The fake candidate who passed every interview) 37:00 – 75 Neo labs: what separates the winners from the two-thirds worth nothing? 40:45 – Is data actually a commodity — and can data providers be $100BN companies? 48:00 – Can you be the referee when the players are paying you? (And Arena's real revenue) 50:45 – Will the model providers eat the application layer? Are Harvey, Lagora and Figma in trouble? 54:10 – Quickfire: Why hasn't NVIDIA bounced on the rise of open source, who hits $10 trillion first, and does the compute debt cycle end in insolvency?    

As building gets easier and almost anyone can ship an app in an afternoon, what actually separates a product leader who creates leverage from one who doesn't?In this special live episode of Supra Insider, recorded on stage at the Toronto Product Conference, Marc Baselga sits down with Colin Matthews, Head of Education for Lenny's Newsletter and a former founder, PM, and longtime hobbyist builder. Marc lays out a thesis that the best way to prepare for the AI era is to build a real side project, one with actual users and ideally revenue, and Colin pressure-tests it with the framework he uses himself: start with distribution, then work backward to the customers and problems you can reach.They explore the blurring lines between product, engineering, and design, why the code review bar stays the same no matter who writes the code, when to ship a PR versus a prototype versus a spec, why Colin uses AI for comprehension rather than raw velocity, and the two distribution paths he has actually seen work for solo builders.If you're a PM wondering which skills matter most next, a product leader trying to get your team building, or anyone who keeps starting side projects that never find users, this episode is for you.Huge thanks to Balaji Gopalan, Andrew Williams, and the team at The Toronto Product Conference for hosting us.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Google didn't ship its big model, but they shipped a TON of new useful AI you can use today. And Google wasn't the only company updating their features behind the scenes. Replit is bringin vibe designing, ChatGPT got a lot more useful on the web, and Meta is changing from chatbot to agent. We'll get you caught up quickly. Chrome adds Some Gemini Spark, Replit Design makes impact, Buzz brings AI Agent Teamwork and 7 more AI Features you Should use Today -- an Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Replit Design Suite Launches With Free MobbinChatGPT Chrome Extension Adds YouTube SummarizationChatGPT Side Chat Integrates Tabs and Highlighted TextMeta AI Rolls Out Recurring Agent TasksGoogle Gemini Generates Images in Google DocsGemini AI Summarizes Comments, Edits in DocsGoogle Gemini Spark Agent Arrives in ChromeChrome Agent Uses Saved Accounts and PasswordsGoogle Lyria 3.5 Music Model ReleasedBuzz by Block Unites Team and Agent CollaborationTimestamps:00:00 Recent AI updates and developments05:01 Creating with Replit and AI models09:42 Real-time research tracking benefits10:34 Meta AI new recurring features13:35 New features of Meta AI17:53 Google Spark integrates with Chrome22:09 Google DeepMind's new music model25:25 Buzz from Block messaging tool29:42 Building a collaborative platform31:23 AI feature updates recapKeywords: Gemini Spark, Google Chrome AI integration, Google Docs AI features, AI image generation, Gemini in Docs, ChatGPT Chrome extension, YouTube video summarization, OpenAI ChatGPT update, Codex, Vibe design, Replit design suite, Mobbin integration, AI reference library, Design export automation, Project management AI, Figma competitor, Replit creative tools, Meta AI, Muse Spark 1.1, Agentic model, Recurring AI tasks, AI scheduling, Daily briefings, AI productivity tools, Google Lyria 3.5, AI music model, Flow Music, Suno, Yudio, AI generated lyrics, Vocal delivery in AI music, Licensing in AI music, Buzz collaboration platform, Block, Square, AI agent teamwork, Slack-like AI platform, Open source collaboration, Agent governance, Cryptographic identity, Agentic browser, Automated web errands, Chrome passwords integration, Google Drive data access, Multi-agent collaboration, Research automation, Enterprise AI workflow, AI productivity boost.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Anthropic ha pasado en unos pocos años de ser el gran reformador de la inteligencia artificial a ser el gran sospechoso. Fundada en 2021 por Dario Amodei y otros ex empleados de OpenAI, Anthropic se presentaba como la conciencia moral de la inteligencia artificial, el laboratorio que anteponía la prudencia a la ambición. Hoy buena parte de Silicon Valley la mira con recelo y constatan que es como cualquier otra, una empresa que compite con las mismas armas que las demás, solo que envueltas en el celofán de las buenas intenciones. El primer aldabonazo llegó en abril con Claude Design, un competidor directo de Figma cuyo consejero delegado, Dylan Field, reprochó en público la falta de sinceridad de un socio con el que habían estado trabajando mano a mano. En junio se sumó Fable 5, la versión restringida del modelo Mythos, que degrada en silencio ciertas respuestas, no admite retención cero de datos y guarda las conversaciones con los usuarios durante 30 días. Al mismo tiempo Amodei se ha puesto en contra de los modelos de pesos abiertos, más baratos y difíciles de controlar. Lo que muchos sospechan es que está creando un pánico para después ofrecerse a resolverlo. El calendario tampoco ayuda, la compañía planea salir a bolsa en otoño y esos modelos abiertos amenazan sus elevados márgenes. Nvidia, Microsoft, Meta, OpenAI, Google y más de 70 empresas han hecho pública una carta abierta en la que defienden los modelos abiertos. Eso ha dejado a Anthropic prácticamente sola. Amodei no ha tardado en responder arguyendo que su oposición se debe a que quiere evitar que gobiernos autoritarios amenacen la seguridad nacional. El otro campo de batalla es China. Hace dos meses, en una carta dirigida a dos senadores, Anthropic acusaba a Alibaba del mayor ataque de destilación del que se tiene noticia. Al parecer Alibaba creo unas 25.000 cuentas falsas que hicieron 29 millones de preguntas a Claude para extraerle información sobre sus mejores capacidades. Lo curioso aquí es que la propia Anthropic ha destilado todo internet sin pagar derechos de autor y ahora exige a los chinos un respeto por la propiedad intelectual que ellos no tuvieron con nadie. Detrás de esta guerra de los pesos abiertos, a las grandes de la inteligencia artificial se les mueve el suelo bajo los pies, pero por otra cosa bien distinta. Están perdiendo mercado. Como veíamos en el programa del lunes, sus clientes han descubierto que no necesitan gastar una fortuna en tokens de un mismo proveedor y están empezando a repartir su consumo entre modelos más caros y capaces y otros más baratos para tareas rutinarias. Así se entiende mejor la doble cruzada que ha emprendido Anthropic contra la destilación china y contra los modelos abiertos. Ambas apuntan al mismo blanco, la competencia barata que machaca sus márgenes, envuelta, eso sí, en el noble lenguaje de la seguridad nacional. En La ContraRéplica: 0:00 Introducción 3:21 Todos contra Anthropic 31:28 El responsable del incendio de Ávila 37:55 La despoblación rural 43:27 Medios e IA · Canal de Telegram: https://t.me/lacontracronica · “Contra el pesimismo”… https://amzn.to/4m1RX2R · “Hispanos. Breve historia de los pueblos de habla hispana”… https://amzn.to/428js1G · “La ContraHistoria del comunismo”… https://amzn.to/39QP2KE · “La ContraHistoria de España. Auge, caída y vuelta a empezar de un país en 28 episodios”… https://amzn.to/3kXcZ6i · “Contra la Revolución Francesa”… https://amzn.to/4aF0LpZ · “Lutero, Calvino y Trento, la Reforma que no fue”… https://amzn.to/3shKOlK Apoya La Contra en: · Patreon... https://www.patreon.com/diazvillanueva · iVoox... https://www.ivoox.com/podcast-contracronica_sq_f1267769_1.html · Paypal... https://www.paypal.me/diazvillanueva Sígueme en: · Web... https://diazvillanueva.com · Twitter... https://twitter.com/diazvillanueva · Facebook... https://www.facebook.com/fernandodiazvillanueva1/ · Instagram... https://www.instagram.com/diazvillanueva · Linkedin… https://www.linkedin.com/in/fernando-d%C3%ADaz-villanueva-7303865/ · Flickr... https://www.flickr.com/photos/147276463@N05/?/ · Pinterest... https://www.pinterest.com/fernandodiazvillanueva Encuentra mis libros en: · Amazon... https://www.amazon.es/Fernando-Diaz-Villanueva/e/B00J2ASBXM #FernandoDiazVillanueva #anthropic #ia Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Charlie Deets (https://x.com/charliedeets) has designed Safari, Arc, and now Dia…Needless to say he knows a thing or two about designing browsers

En este episodio, Oz conversa con Antonio Díaz Cueto —diseñador de producto y fundador de DesignShapers, la mayor comunidad hispanohablante de diseño en la era de la IA— sobre el "gran reset" del mercado de diseño. Hablan de por qué el diseñador promedio ya no tiene lugar, cómo la IA dejó de ser mala diseñando, el diseñador que ahora construye con código (Cursor, Claude Code, Figma Make), el harness engineering aplicado al diseño y qué perfil de diseñador va a desaparecer. Un mapa honesto para reposicionarte en un oficio donde las reglas se están reescribiendo.00:00 Intro y bienvenida03:14 La burbuja del diseño y el "gran reset"07:40 La IA ya diseña bien: qué cambió09:03 No apuestes contra el próximo modelo de IA11:14 ¿Muta el rol del Product Designer?14:02 ¿Sigue siendo Figma el rey?16:27 Harness Engineering para diseño20:45 El stack de Antonio: Claude, Cursor, Codex22:44 "Si no termina en Git, no existe"29:04 La fuente de la verdad se dispersa32:47 El programa de mentoría en la era IA36:12 De colaborar a dominar: producto y código39:33 ¿Qué diseñador va a desaparecer?42:24 Dar valor: pensar como founder46:47 Cierre✩ CURSOS DISPONIBLES

What separates the people who get stronger after a rejection from the people who stay stuck on it?In this episode of Supra Insider, Marc Baselga and Ben Erez drop the guest format for a one-on-one conversation, with Marc interviewing Ben about a piece he started writing on vacation. The trigger was an email from someone who prepared hard, didn't get the role, and said it was impossible not to take it personally. Ben explains why that reaction revealed the real subject was resilience, what he notices when debriefing candidates who bounce back versus those who don't, and why he tells people to transcribe their interviews rather than trust their memories.They explore the four ingredients Ben believes actually cultivate resilience, the one situation where sending a hiring manager more than a thank-you note can swing a decision, and the step-by-step protocol he runs when something falls apart, drawn from three layoffs, a long run of rejections before his Facebook offer, and a torn ACL.If you're coming off a rejection and trying to make sense of it, preparing for interviews and looking for a better way to debrief yourself, or just curious how two operators think about setbacks, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

AI images stop looking generic when creators steer models with real expertise, original references, hand-built assets, and selective craft.Finn McKenty joins Drew Brucker and Rory Flynn to explain why the fastest route to better AI work may involve drawing by hand, reading the manual, and making fewer things. The three alleged efficiency experts also wander into AI burnout, fake UGC, Higgsfield trust problems, Midjourney's long game, and the deeply inconvenient possibility that one great image may beat the 1,000 they can generate before lunch.Topics and tools covered:Midjourney, Weavy, ComfyUI, Claude, Gemini 2.5, GPT Image, Nano Banana, Flux, Krea, Figma, Adobe Photoshop, and Higgsfield anchor the practical discussion. Sketch references, image references, style references, out-of-distribution inputs, material-specific prompting, systems thinking, AI adoption, fake UGC, creator trust, and durable AI-assisted SEO define the core concepts.---⏱️ Fast Hour00:00Who is Finn McKenty?03:13 Why did Finn quit YouTube?05:30 Do creators need a large audience?12:05 Which existing skills create an AI edge?14:26 Does expertise accelerate AI mastery?16:27 Are creatives too attached to process?18:17 Why do companies fail to adopt AI?20:17 Why does advanced AI work cause burnout?27:33 Where should creators invest brainpower?29:31 How do creators steer probabilistic AI?31:27 What made Midjourney click for Finn?34:00 How did AI produce 10x SEO traffic?37:44 Why reject polished AI perfection?38:41 Why does handmade design feel better?41:06 How should AI and manual craft combine?46:24 Why does making more have zero value?50:57 How does material knowledge improve prompts?55:03 Why choose Weavy over ComfyUI?55:44 How do sketches improve AI images?58:33 How should creators divide work with AI?01:02:12 What does out-of-distribution mean?01:05:56 How do original references beat AI slop?01:07:03 Why does fake AI UGC destroy trust?01:11:31 What went wrong with Higgsfield?01:22:33 Why did Midjourney acquire Co-Star?01:24:09 Has Midjourney had the right vision?01:27:30 Why are creators returning to Midjourney?01:29:10 Why does bootstrapping protect creativity?01:34:01 Why does AI creative work need fun again?01:35:04 Does direct attribution kill creativity?#Midjourney #AICreativity #AIDesign #GenerativeAI #FastHours

Peter and Jesse compare notes from Config, Figma's AI study, Lenny's newsletter survey, and countless conversations to read the current state of design leadership. The signals: existential AI panic has cooled into exhaustion, organizational chaos and leadership churn are the new normal, and design still stumbles on business acumen. Their case for thriving—lead, don't just design, and reclaim design's unfinished mandate.

Claude Design went viral at launch, but has been kinda crickets since.

John Lilly has had a front row seat to almost every major shift in technology over the last thirty years, and he has sat in almost every chair while it happened.He was a Senior Scientist at Apple in 1997, when the market cap was two billion dollars and most people assumed the company was finished. He co-founded Reactivity, later acquired by Cisco. He became CEO of Mozilla and led Firefox past 450 million users, running an open source nonprofit at a time when nobody else in Silicon Valley was doing anything like it. Then he spent over a decade as a General Partner at Greylock, where he led investments in Instagram, Dropbox, Figma, Tumblr and Quip. He sits on the boards of Figma, Duolingo and Nuro, and recently joined Gigascale Capital as an Advising Partner, backing companies rebuilding the physical economy.In this conversation with Jessica Neal and co-host Jeff Markowitz, John walks through what he actually saw in the founders everyone else missed.He tells the story of chasing Kevin Systrom for six months, wiring the money on a Thursday, and getting the call three days later that Instagram was selling to Facebook. He explains the moment a friend showed him Instagram and what clicked was not the filters, it was the distribution. And he describes meeting a 19 year old Dylan Field at a Starbucks in Palo Alto, telling him his product did not matter, and then spending the next decade watching him prove it wrong.He is also honest about the parts that are harder to hear. Why some partners at Greylock wanted Dylan replaced. Why he thinks micromanaging the right things is a feature of good leadership, not a flaw. Why he does not tell CEOs anything, and what he does instead. And why, at a moment when OpenAI and Anthropic are pulling talent out of every company on earth, the answer is not matching the money.He closes with something a colleague at Mozilla told him years ago that he still carries: we had a chance to make the world we wanted, and we did it.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━WHAT WE COVER━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━THE FOUNDER QUESTION→ How John spotted Dylan Field at 19→ Why recruiting senior people is the strongest early signal→ The trait every great founder shares: relentless about getting help→ Why non-defensiveness beats raw intelligence→ The lunch where they told Dylan he had to build a go to marketTHE INVESTMENTS→ Chasing Kevin Systrom for six months→ The underwater photographer who explained Instagram to him→ Why the network mattered and the filters did not→ The four day flip to Facebook and what he told his LPs→ The Adobe Killers that came before Figma and why this one was differentLEADERSHIP AND SCALE→ Going from 12 people to a quarter of the internet at Mozilla→ Why he never learned the delegation lesson everyone teaches→ Larry and Sergey reviewing every resume and what that signals→ Learning to be simpler in your message as you grow→ Deciding you would rather be successful than comfortableTHE TALENT WAR→ Why every great person is now a free agent→ The spreadsheet exercise he gives every CEO→ Tour of duty and the bilateral deal between company and employee→ Never making an offer until you know it will be accepted→ Why money is rarely the only reason people leaveTHE CURRENT MOMENT→ Why leadership now means stability without pretending anything is stable→ The collision of faster scaling and total vulnerability→ Why org size no longer scales with impact→ Alpha nerds, Tim O'Reilly, and following the most technical people around→ Investing in transformers, copper recycling and rare earth supply chains→ Why none of the AI future is settled and it is all still to play for━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━CONNECT WITH JOHN LILLYLinkedIn: linkedin.com/in/johnlilly━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━Truth Works is hosted by Jessica Neal, former Chief Talent Officer at Netflix, with co-host Jeff Markowitz.

Live recording of the fortnightly podcast Design Systems WTF (back for season two!), where Luke Murphy and Michelle Chin attempt to combat all the amazing wtf in design systems. In each episode, they answer a single question around design system troubles with a Q&A from the live audience.Governance seems to be one of those fancy words that gets thrown around a lot by design system people - but what level of governance is actually worthwhile, and how much of it is theater? Michelle and Luke will dive into governance, what level is good enough, and what bits they think you should scrap.Show notesBrad Frost's governance flowchart — the component approval flow Michelle borrowed and adapted: A Design System Governance Process. Also available as a Figma community file, and his lighter-touch follow-up: Master design system governance with this one weird trickNathan Curtis on governance and contribution — his writing on contribution models referenced in the episode: Contributions to Design Systems and Team Models for Scaling a Design SystemManaging Chaos: Digital Governance by Design by Lisa Welchman — Michelle's book recommendation for adding process in a way that isn't "gross", with a historical look at how web governance emerged: Rosenfeld MediaOrg Design for Design Orgs by Peter Merholz & Kristin Skinner — Luke's companion-piece recommendation on ritual and process for design teams, transferable to design systems: petermerholz.com / O'ReillyBig Vape: The Rise and Fall of Juul — the Netflix documentary on Juul and "how not to do a startup": Netflix

After an unexpected layoff, Eric Posen went from day-one outreach to three offers and a director role at Super.com in six weeks.In this episode, Eric breaks down the search in detail: how he mobilized his network, why he thinks cold applications are dead in this market, what made a referral effective, and the Claude tools he built to track roles and tailor his résumé.The biggest surprise came across seven interview processes: not one interviewer asked how he used AI. Eric, Marc, and Ben unpack why, along with take-home assignments, taste, negotiating scope and level, and choosing between three offers.This episode will be useful if you're searching for a role, preparing for interviews, or rebuilding your hiring process for the AI era.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Marc Ferrentino, Co-Founder and CEO of Quotient and former chief technical architect at Salesforce, joins Sam Jacobs and Asad Zaman to separate AI-marketing hype from what actually works today. Marc's core claim: the 'AI CMO' is marketing fiction, and the real near-term unlock is clearing the roughly 70% of a marketer's day lost to busywork, so smaller teams can finally operate like big ones. Topics include the collapse of specialized marketing roles into cross-functional generalists, why the apprenticeship model that trained classic marketers is most at risk, where the durable edge moves as performance marketing gets automated... and how taste separates real work from AI slop. Plus, a Quiz Pro Quo trivia round on famous tech emails, an honest look at ageism facing marketers in their 40s and 50s, and a Bulls and Bears debate on legacy martech, HubSpot, and Figma. Key Takeaways: - Marc's near-term promise to marketers is not an AI CMO but the removal of busywork. As he puts it: "companies that come out and claim to be your AI CMO… I think that is the most misleading marketing, but it's also just not true." Buckle up for a story or two about how he's seen the "AI CMO" blow up in people's faces. - The hiring bar is shifting from raw craft to AI fluency. In Marc's words: "we're all looking for the 10x developer, but the 10x developer who knows how to use AI is a 100x developer. And so it's the same thing with the marketer… the marketer who knows how to use AI is a 100x marketer… That's the person we want on the team." And he shares how his own developer interviews changed to match. - Marketing roles are not disappearing, but the specialist ladder that trained seasoned marketers is eroding. As Sam Jacobs, CEO of Pavilion, put it: "I see the collapse of specialization within a particular function. That doesn't diminish the need for the function, but does create an opportunity for those that are more fluent in all of these cross-functional tools." His deeper worry is the pipeline: "the apprenticeship model is the thing that's most at risk," raising the prospect of "a lack of classically trained marketers." - Ageism is real in marketing hiring, and it often hides inside neutral-sounding language. Asad Zaman, CEO of STA, observed, clients are saying things like: "I'm looking for somebody with high energy. High energy is like, I need somebody who is 30 to 33… high energy is like an age, basically." To what extent do the hosts agree with this perspective? Only one way to find out... Connect with the Hosts & Guests: Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/ Host: Asad Zaman, CEO at STA - https://www.linkedin.com/in/azaman1/ Guest: Marc Ferrentino, Co-Founder & CEO at Quotient - https://www.linkedin.com/in/marcferrentino/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 Introducing Marc Ferrentino 02:09 Where AI Actually Works Today 04:05 The 'AI CMO' Is a Lie 07:10 AI Multiplies, Not Replaces 12:11 The Collapse of Specialization 15:10 The Apprenticeship Model at Risk 16:11 The Real Edge Is Brand 21:18 AI Slop and the Taste Problem 23:02 Marketing for the Rest of Business 35:15 Are the Models Asymptoting? 39:01 Quiz Pro Quo 44:44 Ageism in Marketing 49:18 Curiosity over Age 58:34 The 100x Marketer 1:03:54 Bulls VS Bears

AI is making creative production faster while increasing the value of human craft, imperfection, and hands-on creative control.Drew and Rory start with Netflix's 300 AI-assisted programs and somehow end up defending Blockbuster, boxy cars, greasy roommates, and the radical act of making creative work harder on purpose. Between the usual intellectual potholes, they uncover why invisible AI succeeds, why perfect outputs are becoming exhausting, and why human-made work may become the premium signal.Covered in this episode:Netflix generative AI workflows span concept development, pre-visualization, visual effects, post-production, and release. Suno, Udio, Strudel, Foley artistry, AI music licensing, Runway visual storytelling, Claude, ChatGPT, Figma shaders, Photoshop retouching, creative consistency, nostalgic design, imperfect aesthetics, and hybrid human-AI production define the broader creative shift.---⏱️ Fast Hour00:00 Why are guests returning to Fast Hours?03:45 Why does summer trigger nostalgia?10:49 How is Netflix using generative AI?20:39 Could Blockbuster have become Netflix?24:04 What AI tools has Netflix open-sourced?28:36 What did the Suno breach reveal?32:21 Should AI be used to make music?43:31 Breaking down Runway's lamp film—hy does it work?51:43 How many movie story arcs exist?53:32 Does AI increase the value of human craft?57:53 Why are creators rejecting AI perfection?01:06:16 Why is nostalgic design returning?01:17:09 Why do simple stories feel better?01:23:09 How do AI projects maintain consistency?01:25:43 Who should Fast Hours interview next?#GenerativeAI #AIFilmmaking #AIMusic #CreativeProcess #FastHours

Jeremy catches up with friend of the show, Chris Nguyen, who's rebuilt his business twice since his last appearance. They talk about why designers can't count on job security the way they used to, what it actually takes to build something of your own, and why the perfect plan is a myth worth abandoning early.If you can't count on your job to protect you, what are you doing to protect yourself?Chris Nguyen has already rebuilt his business once since he last joined the show, and by the time this episode airs, he'll be in the middle of doing it again. He built UX Playbook into a recognized education brand, tried a community project called Backlog that fizzled out, took several months off to recover, and landed on Rectangles: a live-stream and newsletter brand built around the design conversations he wishes he'd had years ago. None of it happened on a straight line, and that's kind of the point.Jeremy and Chris talk candidly about why designers can't afford to treat a single employer as their whole safety net anymore. Jeremy's own team recently made a drastic tooling change that upended how designers on his team work day to day, and he connects that disruption directly to a bigger argument: if the ground can shift under you that fast, having something outside your job, whether it's a side project, a creative outlet, or a small business, isn't optional anymore. It's how you stay steady.The conversation keeps circling back to a simple, unglamorous truth: nobody has the plan figured out in advance. Chris talks about sitting on the Rectangles idea for the better part of a year before finally committing to a two-week sprint to get it out the door, and admits he still doesn't fully know what it'll become. Jeremy pushes on that idea with his own reflections on burnout, reinvention, and why doing something just for yourself, outside of work entirely, might be the most stabilizing thing a designer can do right now. Give this one a listen if you've been sitting on an idea and waiting for the right moment.Topics:• 03:16 – Catching up on 100 episodes and the never-ending edit grind• 05:33 – What Chris has been building since his last appearance• 06:20 – The funk, the time off, and the shift from product to media• 09:37 – Why running a media company means the content is the product• 11:36 – Chris breaks down what Rectangles actually is• 14:04 – Going beyond UX and UI into big D design• 15:26 – The early 90s aesthetic behind the Rectangles brand• 20:10 – Why Jeremy's team just walked away from their Figma license• 22:42 – Designing straight into Cursor with a component library• 29:20 – The case for building income outside a single job• 32:36 – How UX education content has changed as the industry shifts• 34:22 – The unglamorous side of building something online• 52:56 – Why a latte art post outperformed a bias breakdown on LinkedIn• 57:03 – Chris's closing advice on ideas versus execution• 58:23 – Consumption versus creation and why the balance matters• 58:43 – Where to find Chris and the Rectangles launch detailsHelpful Links:• Connect with Chris on LinkedIn• Subscribe to Rectangles• Get your UX Playbook—Thanks for listening! We hope you dug today's episode. If you liked what you heard, be sure to like and subscribe wherever you listen to podcasts! And if you really enjoyed today's episode, why don't you leave a five-star review? Or tell some friends! It will help us out a ton.If you haven't already, sign up for our email list. We won't spam you. Pinky swear.• ⁠⁠⁠⁠⁠⁠Get a FREE audiobook AND support the show⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠Support the show on Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠Check out show transcripts⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠Check out our website⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠Subscribe on Apple Podcasts⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠Subscribe on Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠Subscribe on YouTube⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠Subscribe on Stitcher⁠

Noam Segal is a longtime research leader across Airbnb, Meta, Twitter, Zapier, Intercom, and Figma, a certified coach, AI builder, and my community research lead. Together, we run the annual Tech Worker Sentiment Survey, now in its second year and one of the largest of its kind: a quantitative study of how people in tech actually feel about their jobs, AI, burnout, and the future of their careers. This year's survey captured responses from thousands of workers across product, engineering, design, research, marketing, data, and sales, and the results are striking.In our in-depth conversation, we discuss:1. Why AI has split the tech workforce almost exactly in half—one half that's thriving, another that's shaken2. The four emotional archetypes defining tech workers right now (the Energized, the Conflicted, the Disoriented, and the Resentful)3. Why burnout has jumped an alarming 11 points in a single year4. Why nobody in tech would recommend their job to someone entering the industry today5. The #1 fear in tech right now (it's not job loss to AI)6. Why managers are the single biggest lever for employee well-being7. Concrete advice for what employees and leaders can do right now—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyMercury—Radically different banking, now with Command: https://mercury.com/command?utm_source=lennys&utm_medium=sponsored_newsletter&utm_campaign=26q3_brand_campaign—Episode transcript: https://www.lennysnewsletter.com/p/how-tech-workers-actually-feel-about—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Noam Segal:• X: https://x.com/noamseg• LinkedIn: https://www.linkedin.com/in/noamsegal—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 Noam Segal(02:34) About the survey: methodology and scope(06:04) The core finding: AI has split the tech workforce in half(13:03) The AI identity stance(14:40) The four archetypes: Energized, Conflicted, Disoriented, Resentful(19:35) Burnout is surging (and why shipping faster is making it worse)(22:53) A glimmer of hope(24:55) Layoff worries(29:15) The career recommendation NPS score(36:45) The ladder metaphor: rungs disappearing beneath our feet(45:14) AI is making us faster, not better(52:53) The #1 fear: being squeezed to do more for the same pay(55:55) The emotional landscape and “smiling exhaustion”(01:01:02) Designers and researchers: the most negative group two years running(01:06:27) Who's happiest(01:12:18) Managers: the single biggest lever on well-being(01:18:47) The industry is “chaotic”(01:24:53) What employees and leaders can do right now(01:31:32) AI guilt and closing thoughts—Referenced:• How tech workers are feeling in 2026: a workforce splitting in two: https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026• How tech's most resilient workers handle burnout: https://www.lennysnewsletter.com/p/how-techs-most-resilient-workers• Please stop the AI Confidence Theater: https://www.elenaverna.com/p/please-stop-the-ai-confidence-theater• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp• NPS Is The Worst: https://www.npsistheworst.com• The Terminator: https://www.imdb.com/title/tt0088247• Skynet: https://terminator.fandom.com/wiki/Skynet• Inside Devin: The world's first autonomous AI engineer that's set to write 50% of its company's code by end of year | Scott Wu (CEO and co-founder of Cognition): https://www.lennysnewsletter.com/p/inside-devin-scott-wu• Devin: https://devin.ai• An AI state of the union: We've passed the inflection point, dark factories are coming, and automation timelines | Simon Willison: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union• Redeploying Fable 5: https://www.anthropic.com/news/redeploying-fable-5• Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble• Inside Linear: Building with taste, craft, and focus | Karri Saarinen (co-founder, designer, CEO): https://www.lennysnewsletter.com/p/inside-linear-building-with-taste• Building beautiful products with Stripe's Head of Design | Katie Dill (Stripe, Airbnb, Lyft): https://www.lennysnewsletter.com/p/building-beautiful-products-with• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• OpenAI Codex lead on the new shape of product work | Andrew Ambrosino: https://www.lennysnewsletter.com/p/openai-codex-lead-on-the-new-shape• Elon Musk: ‘Chances are we're all living in a simulation': https://www.theguardian.com/technology/2016/jun/02/elon-musk-tesla-space-x-paypal-hyperloop-simulation—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

This Week In Startups is made possible by: NetSuite https://NetSuite.ai/TWIST Squarespace https://squarespace.com/twist YSecurity https://YSecurity.io/TWIST Today's show: *America posted 400,000 farm jobs last year, and fewer than 1% got a single domestic applicant. Danny Bernstein of Reservoir believes it's time to start automating this backbreaking labor, like picking stone fruit in 110°F temperatures. So he built the world's first on-farm robotics incubator on 40 acres of California farmland. Here's why he argues that automating farms isn't just a new opportunity, it's a national security issue. PLUS Jason responded to Gal Shir's viral "AI beat me at design" tweet, got into a kerfuffle with Figma CEO Dylan Field, and wound up making a brand new J-Trade. AND we're talking about the Dept. of Education's new "do no harm" policy, the Brown University AI cheating chart that's blowing up social media, and Lon has fresh streaming recommendations in an all-new Off Duty. Guest: Danny Bernstein on X: https://x.com/bernsteind Reservoir Farms: https://reservoir.co/ Reservoir VC: https://reservoir.vc/ Relevant Links: Bonsai Robotics: https://bonsairobotics.ai/ Root AI acquired by AppHarvest: https://www.therobotreport.com/root-ai-acquired-by-appharvest-for-60m/ John Deere: https://www.deere.com/en-us/ Western Growers Association: https://www.wga.com/ Tanimura & Antle: https://www.taproduce.com/ Naturipe Berry Growers: https://www.naturipefarms.com/ Driscoll's: https://www.driscolls.com/ Taylor Farms: https://www.taylorfarms.com/ The Wonderful Company: https://www.wonderful.com/ Capital Factory: https://www.capitalfactory.com/ Gal Shir "quitting design" post: https://x.com/galshirart/status/2074854464729629060 Dylan Field response to Gal Shir: https://x.com/zoink/status/2075290218660298807 JCal response to Field and "J-Trade": https://x.com/Jason/status/2075481565115654305 Figma: https://www.figma.com/ Dept. of Education: "Do No Harm" policy announcement: https://www.ed.gov/about/news/press-release/us-department-of-education-issues-final-rule-hold-all-colleges-and-universities-accountable-low-earning-programs NPR coverage on Dept. of Education earnings test: https://www.npr.org/2026/06/30/nx-s1-5835631/turner-camhi-do-no-harm-college-loans Paul Graham "cheating chart" post: https://x.com/paulg/status/2075031014628311236 Off Duty Recommendations: "Lioness" on Paramount+: https://www.youtube.com/watch?v=jNRQ0PR4a8U "Mayor of Kingstown" on Paramount+: https://www.youtube.com/watch?v=VkQzvwxOp0s "Human Vapor" on Netflix: https://www.youtube.com/watch?v=7xe6dRKVAb8 "Sugar" on Apple TV+: https://www.youtube.com/watch?v=twvPGxuEOEA "Wind River" (now on Netflix): https://www.youtube.com/watch?v=CZgN0dpFoaE "Not Fade Away" by Peter Barton & Laurence Shames: https://www.amazon.com/Not-Fade-Away-Short-Lived/dp/1579546889 Timestamps: 0:00 Jason's ongoing World Tour 1:43 Danny Bernstein joins live from Reservoir Farms 3:38 What is "specialty crop agriculture" 5:15 Why strawberries are the "white whale" of AgTech 6:55 The labor crisis in farming 9:20 Why AgTech never scaled 9:51 NetSuite - For the first time, you can try NetSuite Next for free. If your revenues are at least in the seven figures, go to https://NetSuite.ai/TWIST 10:51 Inside Reservoir's business model 17:44 Agriculture as national security 20:09 Squarespace - Turn your idea into a beautiful website! Go to https://www.squarespace.com/twist for a free trial. When you're ready to launch, use offer code TWIST to save 10% off your first purchase of a website or domain. 22:15 Did AI beat a designer at design? 30:56 YSecurity - The on-demand security team for startups. Need enterprise-grade security without hiring a $400k CISO? YSecurity gives you 40+ expert engineers, matched to exactly what you need, by the hour, with your first six hours completely free. Go to https://YSecurity.io/TWIST 37:23 The "do no harm" college earnings test 43:27 Brown University students cheated on their midterms 52:22 Lon's streaming recommendations 59:46 Jason's new snake grabber   Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Every designer you know is quietly panicking about the same thing: their Figma hours dropped from 90% of the week to 30%, and nobody told them what to do with the other 60%. Is that a crisis or a promotion?Maria Irie runs brand at Chili Piper with a team of two — and she's spent the last year rebuilding her entire job around AI without losing what makes the brand feel human. She's mid-migration from Webflow to Claude, she's turned herself into a systems-builder instead of a one-pager machine, and she's got a theory about why the scrappiest, least "scalable" parts of marketing — the Ibiza offsites, the events, the stuff that doesn't show up in a report — are exactly what AI can't touch. This isn't a "prompt better" conversation. It's what happens when a whole discipline gets rewritten mid-career.We also cover:Why Maria cut her Figma time from 90% to 30% — and what she's building insteadThe "too many windows" problem: what it actually costs your brain to run five AI tools at onceWhy a two-person brand team deliberately protects the stuff that doesn't scale — and bets the whole brand on it

This Week In Startups is made possible by: Digital Ocean - do.co/twist Agree.com - agree.com Every.io - every.io.   Today's show: How many startups matter in tech? Fewer than you think. That's why venture capitalists are tripping over themselves to get onto their cap tables, no matter the cost. Why? Footwork's Nikhil Basu Trivedi argues that the Valley has never been more "power-law-pilled" than it is today. Basu Trivedi joined Cendana Capital's Michael Kim and TWiST's Alex Wilhelm to go deep on secondary markets, the state of startup M&A, why the SaaSpocalypse may be temporary, and what could trigger a retrenchment of the AI trade. It's Wednesday, so it's time for our venture capital roundtable to go deep on how VCs are investing today, and where on the horizon they have their eyes fixed! Guest links: Nikhil Basu Trivedi https://x.com/nbt Footwork https://www.footwork.vc/ Michael Kim https://x.com/MKRocks Cendana Capital https://www.cendanacapital.com/ Show links: The USVC-Anduril blowup https://x.com/ankurnagpal/status/2072701195714531398 Kline Hill Cendana Partners https://www.secondariesinvestor.com/kline-hill-and-cendana-raise-400m-for-second-vc-secondaries-fund/ GPTZero's exit https://gptzero.me/news/preserving-whats-human/ Salesforce buys Fin https://www.salesforce.com/news/press-releases/2026/06/15/salesforce-signs-definitive-agreement-to-acquire-fin/ Vercel buys Better Auth https://vercel.com/blog/vercel-acquires-better-auth Figma buys Bud https://techcrunch.com/2026/07/07/figma-acquires-team-behind-a-vibe-coding-app/ Protoge https://withprotege.ai/ Windborne https://windbornesystems.com/ Etched https://www.etched.com/ Lovable's reported raise https://sifted.eu/articles/lovable-300m-13-2bn-valuation Josh Browder https://x.com/Joshuabrowder   Timestamps: 0:00 Introduction: Nikhil Basu Trivedi (Footwork) & Michael Kim (Cendana Capital) 1:59 The Anduril vs. USVC secondary market blowup 4:08 Why Silicon Valley is 'power-law-pilled' 8:23 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off! 9:37 Information asymmetry in the secondary markets 9:45 Every.io — For all of your incorporation, banking, payroll, benefits, accounting, taxes or other back-office administration needs, visit https://every.io 15:02 Is SPV fraud smoke or fire? 16:32 Superhuman acquires GPTZero 19:54 Agree.com - Stop chasing invoices and automate your entire contract-to-cash stack. Go to https://agree.com and tell them Jason sent you to get 50% off for life! 21:10 The M&A wave 27:19 The SaaSpocalypse debate 29:59 DigitalOcean - Head to https://do.co/twist to start building on DigitalOcean's AI-Native Cloud today — and cut your AI workload costs by up to 50%. 30:44 Data's moment in the energy → compute → data loop 35:01 Where will AI value accrue? 40:04 What could cause an AI correction? 42:17 Why some companies are "too big to miss" 46:23 China's possible open-weight model ban 53:28 Young founders: Etched, Thiel Fellows, Z Fellows, Neo 55:33 Portfolio spotlight: WindBorne's weather balloons and data moat 58:47 Michael's favorite fund manager: Josh Browder Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Anthropic se está comiendo a sus propios clientes. Figma la acusa de robarse su tecnología después de meses de trabajar juntos. El gobierno de Estados Unidos ya no confía en tener sus modelos de IA en manos de terceros y contrata a Palantir para construir los suyos propios con hardware propio.Carlos Muñoz y Ricardo Moreno destapan el caso que tiene a Silicon Valley hablando: cómo una empresa de inteligencia artificial terminó compitiendo con quienes la contrataron, por qué esto es distinto a lo que hizo Meta o Google en su momento, y por qué el "Memory Trade" de Micron movió miles de millones en la bolsa el mismo día que nadie lo vio venir.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

This Week In Startups is made possible by: Northwest Registered Agent https://northwestregisteredagent.com/twist Vanta https://www.vanta.com/twist Sentry https://sentry.io/twist Today's show: *There are $100 trillion in global assets sitting on top of what Hanover Park co-founder/CEO Chris Hladczuk calls "human duct tape": armies of accountants in offices patching together work from various legacy tools (QuickBooks, Excel) that are holding funds' own data hostage. Can all of this be replaced with AI? Find out how their startup went from overseeing $1B to $20B in assets in just 15 months. PLUS, we flash back to March 2020, when Jason and Figma co-founder/CEO Dylan Field broke down the design tool's initial go-to-market strategy, made some WILDLY inaccurate COVID predictions, and considered anxiety about "SaaS burnout" years before the category went full apocalyptic. Guests: Chris Hladczuk on X: https://x.com/chrishlad Hanover Park: https://www.hanoverpark.com/ Dylan Field: https://x.com/zoink Figma: https://www.figma.com/ Relevant Links: Turner Novak on X: https://x.com/TurnerNovak Banana Capital: https://www.bananacapital.vc/ Emergence Capital: https://www.emcap.com/ Lux Capital: https://www.luxcapital.com/ Susa Ventures: https://susaventures.com/ Bill.com: https://www.bill.com/ METR: https://metr.org/ Granola AI note taker: https://www.granola.ai/ Vanta: https://www.vanta.com/ Foo Camp on YouTube: https://www.youtube.com/c/foocamp TechCrunch Mahalo coverage: https://techcrunch.com/2014/01/27/inside-mobile-news-launch/ Timestamps: 0:00 Hanover Park & the fund admin problem 3:32 Why funds outsource instead of building 5:44 Why fund accounting is so complex 10:38 The "one-click migration" goal 10:48 Northwest Registered Agent - Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at https://northwestregisteredagent.com/twist 13:51 Context vs. intelligence gaps 16:11 No PMs, No Designers 20:46 Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist 23:18 This is a $100T opportunity 25:36 Flashback w/ Dylan Field of Figma 29:15 Sentry - Your team should be focused on shipping features — not chasing down bugs. New users can get $240 in free credits when they go to https://sentry.io/twist and use the code TWIST 31:22 Pre-AI enterprise security worries 36:30 SaaS overload and SaaS burnout 37:30 The evolution of Figma pricing 42:38 The rise and fall of Mahalo dot com 51:38 The work-from-home revolution begins Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Thank you to our partners: (0:00) PARTNER - AD BLURB (0:00) PARTNER - AD BLURB (0:00) PARTNER - AD BLURB Check out all our partner offers: https://partners.launch.co/ Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Klaviyo has been rolling out something called Composer in beta - an AI agent that promises to build entire marketing campaigns from a single prompt. Audience, copy, design, segmentation, all of it. The promise is impressive. So I got access on a real client account and tested it myself. And my honest take is more nuanced than the hype - and more useful than the skeptics would have you believe. The short version: Composer is less an autonomous agent and more a really well-informed assistant. It knows your account data better than any outside AI tool could. It just still needs you. Where it genuinely surprised me was the data side - send time recommendations backed by actual account history, revenue per recipient, open rates by date, flagging which sends had enough volume to be statistically meaningful. That's marketing expert level analysis, and it's specific to your account in a way that ChatGPT or Claude simply can't replicate without a direct integration. Where it fell short was the creative side. The copy was functional but generic. The design pulled image slices from across the account that didn't really hang together. Getting it to feel on brand would have taken enough back and forth that I may as well have just written and designed it myself. In this episode I walk through exactly what happened when I tested it, what genuinely impressed me, where it still needs a human, and how to think about using it if you get access. ✨ In this episode, you'll learn: What Klaviyo Composer actually is and what it promises to do What happened when I tested it on a real client account with a real campaign prompt Why the creative output is a starting point, not a finished product The limitation that affects accounts where most emails are designed in Figma or Canva The one thing Composer did that genuinely surprised me - and why it matters Why the send time analysis is where this tool actually earns its place What to watch out for with the auto-populated segmentation suggestions How to think about the human vs. AI division of labor in your email program right now Let it handle the data. You handle the creative direction and strategy. That framing is really useful for anyone thinking about where AI fits into their email marketing going forward. Work with Joy Joya: https://joyjoya.com

Most creator marketing advice is about going viral, but real impact comes from how brands and creators actually work together.In this episode of Stop the Scroll, Roo Yeshpaul Johnson, Head of Creator Marketing at Figma, and I are talking about what's shaping the creator economy right now — from the myth of unlimited brand budgets to why “great creator partnerships” often come down to something much less glamorous: reliability, negotiation, and whether the math still works.We also get into the tension no one wants to say out loud: creators are getting more expensive, brands are getting more selective, and long-term partnerships are not as guaranteed as the internet makes them sound.And underneath all of it is a bigger question — if everyone is a creator now, what actually makes someone worth paying attention to?Highlights:(00:00) Introduction(01:18) Meet Roo Yeshpaul Johnson(03:41) Why Marketers Still Need a Seat in the Room(07:14) What Creator Activations Look Like Without Marketers(08:48) The Myth of Unlimited Creator Budgets(15:30) What Makes a Creator Easy to Work With(19:45) The Coming Tiered Creator Economy(25:17) What Will Feel Weird About Creator Culture Soon(27:42) Rise of the Practitioner Creator(30:10) Why Creator-Brand Relationships Break or Scale(32:23) What Every Marketer Should KnowResources:Hear more from me in the Stop the Scroll Newsletter: https://briannadoe.substack.com/Connect on LinkedIn: https://www.linkedin.com/in/brianna-doe/ Roo's LinkedIn: https://www.linkedin.com/in/rooyj Figma LinkedIn: https://www.linkedin.com/company/figma Figma Website: https://www.figma.com/ 

We're back with more from our live event at the Yerba Buena Center for the Arts in San Francisco. In this episode, we sit down with Dylan Field, a founder and the chief executive of the design company Figma, for what he describes as a “roller coaster” of a conversation. We cover everything from the company's “Design Is Dead” campaign to the sudden resignation of the Anthropic executive Mike Krieger from Figma's board. Then, we close things out with a special musical performance by eight wooden robotic dolls that make up the Teenage Engineering Choir. One quick correction to note: In our interview with Field, he makes reference to the SpaceX S-1 filing and misstates what the company says their addressable market for A.I. enterprise applications is. Field says “$22.9 trillion,” but the correct number from the SpaceX filing is $22.7 trillion. The decimal point makes it look small, but it's a difference of $200 billion. We'll be back on Friday with our final installment of “Hard Fork” Live.   Guests: Dylan Field, chief executive and co-founder of Figma. Dan Powell, robot conductor, New York Times music composer and “Hard Fork” theme-song creator. Teenage Engineering Choir   Additional Reading: This Start-Up's $20 Billion Sale Died. It Came Fighting Back.   We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Daniel Mahncke and Shawn O'Malley take a deep dive into Wix.com — the Israeli website-building platform whose investment case now turns on two of the most debated questions in the stock today: whether the generative-AI wave that lets anyone spin up a site from a text prompt is the end of Wix or whether Wix is too sticky, and whether the Base 44 acquisition — Wix's bet on AI-powered app generation — is the next leg of the story or a distraction from the SMB infrastructure business the company already dominates. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:01:32) How Wix was founded (00:21:35) Why clients keep using Wix (00:28:05) How much of WIX is actually vulnerable to AI (00:37:07) Why Wix is more sticky than it seems (00:38:24) Whether vibecoding is likely to disrupt drag-and-drop website building (00:46:54) Why Base44 could change the entire investment case (01:06:24) How Wix could survive and turn into a multibagger (01:09:21) Valuation discussion of Wix (01:13:26) Whether Shawn and Daniel add Wix to the Intrinsic Value Portfolio BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠. Portfolio Review Submit Tool. Value Investor Club Article. Chit Chat Stocks w/ Manuel Cunha. Future Investing Interview w/ Manuel Cunha. Rene Sellman Substack Article. Manuel Cunha Substack Article. Previous Intrinsic Value breakdowns: Figma, Microsoft, Salesforce, Adobe. Follow Shawn on ⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠. Follow Daniel on ⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠sponsors⁠: Plus500 Netsuite Shopify Vanta References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm