Podcasts about Figma

This episode contains some screen sharing so it's best watched on YouTubeWhat happens when one product leader decides to stop copy-pasting between chat windows and instead build an operating layer that puts coding agents in the hands of an entire company?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Kyler Ross, Head of Product at Cloaked, to walk through the internal “harness” he started building last Thanksgiving: an agent-friendly system of context files and scripts that lets agents read from and write to the team's real tools. Kyler explains how it gets installed on every company machine, why he treats each new agent session like onboarding an employee, and how a self-improving loop of skills and automated reviews keeps it getting better.They explore his day-to-day setup for running many agents at once, why worktrees and Claude Code hooks exist to make failure nearly impossible, a one-on-one prep skill that pulls context from every corner of the company, and the layered guardrails, including a nightly “librarian” agent, that keep confidential information from leaking.If you're a product or engineering leader trying to make your team more AI-native, someone wiring agents into real workflows, or anyone wrestling with how to run agents safely at scale, 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

Season 3 is here with new co-host Lily. This episode tackles a question every designer should sit with: are you mastering your craft, or just mastering your tools?They dig into why "Figma" isn't a skill, designers vibe coding their own tools, and what a pro photographer with a Barbie camera teaches about fundamentals. Plus: why cross-platform handoffs are still broken, what AI agent demos conveniently skip, and the Ferrari Luce debate, where brand legacy meets bold new design.00:00 Intro: Welcome to Season 300:14 Craft vs. Tooling: The Core Debate01:18 Building Your Own Tools03:40 Analog vs. Hyper-Future05:22 Tooling That Incentivizes Better Design06:49 Designing for Context, Not Just Platform08:22 Cross-Platform Continuity & The AI Demo Problem18:24 Bringing It Back: What Is Craft, Really?20:09 How Tool Companies Captured Design Culture22:45 The Ferrari Luce: When Brand Meets New Craft27:00 Digital vs. Tactile in Car Interiors32:37 Wrap-Up: Own the Vertical

L’IA va-t-elle révolutionner la conception de sites web, ou les promesses dépassent-elles la réalité ? Olivier Sauvage, consultant et stratège du web, invité du live Visionary Marketing du 18 juin 2026, a apporté une réponse nuancée, documentée et parfois à contre-courant des discours dominants. Tour d’horizon des promesses réelles, des limites concrètes et des impacts sur les métiers. Sites Web, l’IA à toutes les étapes mais pas (encore) de miracles Sites Web conçus avec l’IA : Si Olivier Sauvage confirme que l’IA est présente à tous les instants, il pense néanmoins elle n’est cependant pas magique. Image d’Olivier Sauvage réalisée par lui-même sur son générateur d’images (oliviersauvage.com) La première question méritait d’être posée franchement : allons-nous continuer à faire des sites web ? La réponse d’Olivier Sauvage est catégorique : oui, et même davantage qu’avant. Les sites web ne disparaissent pas. Ils changent de rôle. L’IA est aujourd’hui présente à tous les stades de la chaîne de production web : outils de design (Figma intègre l’IA depuis longtemps), outils de prototypage, outils de retouche graphique (Photoshop), outils de test, outils de réflexion et de génération de contenu. On ne peut plus y échapper. La question n’est plus de savoir si l’on va intégrer l’IA, mais comment, à quel moment, et à quelles fins. En trois minutes, Stitch va te sortir des pages web là où il faudrait une journée ou deux pour un UX designer. Très impressionnant. Mais en réalité, ce n’est pas un outil qui a cette compréhension des choses que peut avoir un être humain quand il crée des maquettes. — Olivier Sauvage L’IA est un outil qui ouvre des horizons, explore des pistes qu’on n’aurait pas eu le temps d’explorer, accélère certaines phases de production. Ce n’est pas un substitut au métier. IA pour les sites web : les usages les plus solides aujourd’hui Le prototypage : un vrai gain C’est probablement l’usage le plus solide identifié par Olivier Sauvage. Le prototypage, notamment sur des applications mobiles ou des fonctionnalités complexes, était autrefois laborieux. Aujourd’hui, un outil comme Google Stitch permet de générer en quelques minutes des maquettes multi-supports (desktop, tablette, mobile) d’un niveau de réalisation crédible. L’avantage n’est pas seulement la vitesse : c’est la capacité à tester 4 ou 5 variantes là où l’on n’en produisait qu’une seule. On peut explorer des parcours utilisateurs différents, tester des architectures de navigation, obtenir un premier retour client sur quelque chose de visuellement représentatif, et ce bien avant d’engager un budget de développement. La génération d’arborescences et de tree-testing (test de tri de cartes) Autre usage robuste : la définition d’arborescences et le card sorting (tri de cartes, technique qui consiste à demander aux utilisateurs de classer des contenus pour identifier la structure de navigation la plus intuitive). L’IA fait gagner un temps considérable sur ces tâches de structuration de l’information, à condition d’alimenter l’outil avec des données suffisamment riches et spécifiques. Des personas génériques, sortis de nulle part, n’ont que peu de valeur. Des personas connectés à de vraies données de terrain, c’est une autre affaire. Avec l’IA, la conception des sites web n’a jamais été aussi rapide ni gratifiante, mais les itérations et les tests humains restent nécessaires nous dit Olivier Sauvage.Il ne faut pas rêver aux miracles et les web designers ne seront pas remplacés par Merlin l’enchanteur. Image réalisée avec Midjourney. La production d’interfaces : utile, avec supervision La production et la création d’interfaces bénéficient clairement de l’IA. Générer des composants, des variantes graphiques, des systèmes de design : tout cela est désormais accessible plus rapidement. Mais la supervision humaine reste indispensable pour valider que ce qui est produit correspond à la réalité de l’expérience utilisateur attendue. Ce qui ne fonctionne pas (encore) Simuler un comportement humain : une limite fondamentale Olivier Sauvage est catégorique sur un point : l’IA ne peut pas simuler un comportement utilisateur réel. Des personnages artificiels censés tester un site web à la place d’utilisateurs humains ? Je pense que ça ne marchera vraiment jamais. Il y a trop d’inconnues, trop de paramètres. Une IA se nourrit de ce qui existe. Elle ne sait pas ce qui est bon ou pas bon. Elle définit statistiquement ce qui est majoritaire, ce qui n’est pas un gage suffisant de qualité. — Olivier Sauvage Ce point est crucial : le web regorge d’interfaces médiocres. Une IA entraînée sur ce corpus va reproduire ces médiocrités avec une belle régularité statistique. Les sites 100 % IA : pour quels usages ? La question des sites entièrement générés par IA a été soulevée par un participant au live. Le verdict d’Olivier Sauvage est mesuré. Pour un site vitrine informatif d’une TPE locale, un site e-commerce B2C classique, c’est jouable, à condition d’une vérification humaine minimale. Pour un site B2B avec de la vente complexe, des parcours privés, une expérience riche, des animations : la limite est atteinte rapidement. « Les données nécessaires pour recréer des parcours UX valables sur du B2B complexe n’existent tout simplement pas en quantité suffisante. » Et le contenu ? C’est là que le bât blesse le plus. Le « slop » (contenu IA générique, interchangeable et sans valeur ajoutée) est déjà un problème visible. Générer des milliers d’articles en quelques minutes ne crée pas de valeur. Les moteurs de recherche et les utilisateurs s’en aperçoivent. Ce mouvement a un temps limité. La maintenabilité : le problème qu’on ne voit pas tout de suite J’ai cité un exemple vécu : un site d’association refait en 4 heures avec Claude, fonctionnellement supérieur à l’ancien, design convenable. Mais « le jour où la personne qui a développé ça s’en va, on fait quoi ? Qui va le retoucher ? Où est la base de données ? Quels sont les mots de passe ? » La dette technique invisible est l’un des vrais risques du vibe-coding (développement par description en langage naturel, sans écrire de code ligne par ligne) appliqué à des projets réels. Olivier Sauvage va plus loin en suggérant que les solutions no-code (outils permettant de créer des applications sans programmer, via des interfaces visuelles), moins spectaculaires mais structurellement plus solides, méritent d’être reconsidérées dans ce contexte. Des outils comme Airtable, Bubble ou TimeTonic offrent des garanties de maintenabilité que le code généré par IA ne peut pas toujours assurer. L’agent IA et l’avenir du e-commerce Un échange particulièrement intéressant a porté sur le protocole MCP (Model Context Protocol) et l’IA agentique appliquée au e-commerce. L’hypothèse est la suivante : demain, un agent IA pourra conduire une recherche produit, comparer des offres, poser des questions complémentaires, et passer à la transaction en ne donnant la main à l’utilisateur humain qu’au moment du paiement. Cela existe déjà partiellement : Shopify a adopté MCP, et ChatGPT intègre des fonctions marchandes dans certaines géographies. Ce qui change, c’est le rôle du site web : il reste indispensable, non plus comme destination première de navigation humaine, mais comme source de données structurées pour les agents IA. « Le site web va avoir encore une grande fonction : alimenter les IA par ses contenus. » Et Olivier Sauvage ajoute un point prospectif important : les marchands ont de plus en plus intérêt à produire des contenus spécifiques, propriétaires, qu’on ne peut trouver que sur leur site, et qui constituent une vraie barrière à l’imitation par l’IA générique. Premier signal concret de cette évolution : lors de ce live, j’ai mentionné la réservation d’une session photo dans mon studio par un client dont la recommandation initiale provenait de ChatGPT. Le trafic issu des LLM reste marginal, mais sa qualité est notable. Selon le rapport Adobe Digital Insights d’avril 2026, basé sur plus d’un milliard de visites e-commerce, le trafic provenant des LLMs convertit 42 % mieux que le trafic non-IA chez les retailers américains. Semrush va plus loin et mesure un ratio de 4,4× sur certains segments B2B logiciel, avec des taux de conversion de 15,9 % pour ChatGPT contre 1,76 % pour Google organique. Ces chiffres restent toutefois à nuancer : une étude Amsive portant sur 54 sites (septembre 2025) indique que 41 % des sites de l’échantillon convertissaient moins bien via LLM que via l’organique classique. Le résultat dépend du secteur et de la maturité du site. Impacts sur les métiers du design web Une transformation plus qu’une accélération Olivier Sauvage formule ici une thèse importante : l’IA transforme le métier de designer plus qu’elle ne l’accélère. Les gains de productivité purs ne sont pas aussi évidents qu’annoncés. On fait un prompt, on voit le résultat, on se dit c’est révolutionnaire. Puis en réalité, avant d’arriver à quelque chose de vraiment utilisable, on a fait 50 prompts, ce n’est jamais parfait, il faut mettre les mains dans le cambouis. — Olivier Sauvage La comparaison avec le développement est éclairante. Côté demande globale, les données TalentNeuron montrent que les offres d’emploi pour développeurs de logiciels ont progressé de 22 % entre 2023 et 2024, éclipsées toutefois par une hausse de 148 % sur les profils ingénieurs IA et machine learning. Mais côté marché français, la note de conjoncture de l’INSEE de mars 2026 dresse un tableau plus nuancé : l’emploi des moins de 30 ans dans l’informatique et les services d’information a reculé de 3 % entre 2023 et 2025, avec ‑7,4 % d’emploi des 15‑29 ans sur un an au T4 2025. Les entreprises produisent davantage, mais avec moins de juniors, remplacés en partie par l’IA sur les tâches répétitives. Les seniors, eux, passent plus de temps à corriger, structurer et documenter le code généré automatiquement. Ce n’est pas forcément moins de travail : c’est un travail différent. Une étude METR de juillet 2025 a même mesuré que des développeurs expérimentés étaient en réalité ralentis de 19 % avec Cursor Pro et Claude, alors qu’ils estimaient avoir gagné 20 % de productivité. L’écart entre la perception et la réalité est significatif. Olivier Sauvage : même avec l’IA, pour concevoir des sites web, un mauvais ouvrier aura toujours de mauvais outils. Image réalisée avec Midjourney. Conception de sites web, la valeur reste chez les humains, pas dans l’IA Si n’importe qui peut générer des contenus ou des maquettes avec des prompts basiques, et que tout le monde peut le faire, ça n’a aucune valeur puisqu’il n’y a plus de rareté. — Olivier Sauvage La valeur se déplace, elle ne disparaît pas. Elle se concentre chez ceux qui savent poser les bonnes questions, orienter la machine, valider les résultats, et comprendre ce qu’un utilisateur humain ressent vraiment face à une interface. La métaphore du pont en métal du XIXe siècle, évoquée par Olivier Sauvage, est saisissante : les premiers ingénieurs qui ont travaillé avec ce matériau ont simplement reproduit ce qu’ils savaient faire en bois. Ils ont manqué l’essentiel. Beaucoup font de même avec l’IA aujourd’hui. Ce n’est pas sans rappeler le paradoxe de productivité de Solow, formulé en 1987 : « on voit l’ère informatique partout, sauf dans les statistiques de productivité. » La récente étude du NBER (Working Paper n° 34836, février 2026), conduite auprès de près de 6 000 dirigeants aux États-Unis, au Royaume-Uni, en Allemagne et en Australie, confirme que ce paradoxe se répète avec l’IA générative : neuf entreprises sur dix n’ont constaté aucun impact mesurable de l’IA sur leur emploi ou leur productivité au cours des trois dernières années. L’innovation, seul horizon vraiment nouveau Ce qui change fondamentalement, c’est la capacité à innover : tester des concepts qu’on n’aurait pas osé prototyper faute de temps et de budget, explorer plus de pistes, itérer plus vite. « C’est là qu’il faut aller chercher la valeur de ce métier. » Un mauvais ouvrier aura toujours de mauvais outils La conclusion de cet échange est peut-être celle que les formations et les discours sur « l’IA pour tous » négligent le plus : la qualité de l’utilisation d’un outil dépend de la maîtrise du métier sous-jacent. Ce qui fait la différence, c’est la connaissance du métier, l’intuition, la capacité à orienter la machine et surtout le travail de préparation des processus. Un prompt répété à l’identique à chaque session, c’est réinventer l’eau tiède. Un workflow (flux de travail structuré et documenté) efficace, c’est une vraie pratique professionnelle. Ce n’est pas parce que tu as Claude ou un outil de design IA entre les mains que tu vas faire un super site avec une super UX. Tu y arrives parce que tu as les compétences pour comprendre ce qui se passe, pour tester, pour valider, pour te rendre compte que tes utilisateurs comprennent bien ce que tu as fait. — Olivier Sauvage En conclusion sur la conception de sites web avec l’IA Les sites web ne disparaissent pas. L’IA ne remplace pas le designer UX. Les outils IA offrent des gains réels dans le prototypage, la génération d’interfaces et l’exploration créative. Mais la valeur reste chez les professionnels qui savent s’en servir, et non dans les prompts magiques qui génèrent un site en trois minutes. Comme nous l’observons régulièrement sur Visionary Marketing, la réalité de terrain est toujours plus nuancée que les discours à l’emporte-pièce, en bien comme en mal. Pour aller plus loin, retrouvez le blog d’Olivier Sauvage oliviersauvage.com Voir le live en intégralité sur YouTube ▶ Voir le live sur YouTube The post Sites Web, l’IA est omniprésente, mais pas magique appeared first on Marketing and Innovation.

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.

Dans ce 194ème épisode de DigiClub powered by Huawei, Ooredoo Business et Bac Pay, nous bousculons une idée reçue : non, l'innovation technologique n'est pas l'unique solution pour réussir son business. En compagnie de notre invitée Inès Chniour, Design Thinker et CEO de COMITT, nous analysons pourquoi le fait de brûler du cash dans des solutions techniques complexes ou des plateformes numériques sans comprendre l'humain mène droit dans le mur. À travers des exemples concrets (du secteur bancaire aux startups en Tunisie), Inès nous explique comment le Design Thinking permet de recentrer le business sur l'utilisateur final, de tester des prototypes à moindre coût et de valider ses idées avant de dépenser le moindre centime. Au programme : 00:40 Qu'est-ce que le Design Thinking ? (La conception centrée sur l'humain) 03:13 Une philosophie de vie et l'évolution historique des outils 05:25 L'esprit critique et l'importance de la cohérence dans le design 07:04 Pourquoi les dirigeants doivent intégrer cette logique avant la production 08:35 Le parcours d'Inès Chniour : de la Tunisie au Canada 10:50 L'évolution de la relation homme-machine et l'importance de l'expérience utilisateur (UX) 12:20 Étude de cas au Canada : restructurer l'orientation universitaire par l'écoute 16:40 Au-delà du service : prendre en compte l'environnement global de l'utilisateur 20:25 La culture des entreprises familiales en Tunisie et le management par l'empathie 23:25 Retour sur la fidélisation des collaborateurs 25:35 Pourquoi Inès a choisi de revenir en Tunisie après la Révolution 27:45 L'observation du terrain tunisien et les collaborations avec le secteur public 31:10 Travailler en collectif : la force de l'équipe pluridisciplinaire 32:45 Étude de cas dans le secteur bancaire : l'erreur classique du déploiement des ATM 36:15 L'importance cruciale du prototypage et des tests (Figma, Wireframes) avant d'investir 39:35 La vision court-termiste du profit vs la rentabilité par la conception 41:25 Les coulisses des agences : réflexion stratégique vs exécution 43:10 Accompagner une startup en cybersécurité : redéfinir la proposition de valeur 46:50 Savoir lâcher prise face aux choix du client 48:15 L'impact de l'Intelligence Artificielle (ChatGPT, Claude, Gemini) sur la réflexion 51:20 IA vs Humain : pourquoi la tech a besoin du contexte et du sens critique 55:00 Les conseils clés pour tester son idée de business à moindre coût 59:45 Ethique et visions du futur : quel humain crée-t-on pour demain ? 01:03:00 Concevoir pour les minorités : la clé pour créer un produit exceptionnel pour tous 01:05:45 Ce que le Design Thinking apporte sur le plan personnel et professionnel 01:06:35 Les prérequis pour se former et conclusion de l'épisode

Figma's Q1 2026 revenue grew 46% YoY to $333M, with net dollar retention hitting a multi-year high of 139%. But even after its post-IPO slide, the stock still trades at ~75x next-12-month free cash flow. We break down what's driving the valuation gap, why free cash flow per share is still feeling IPO dilution effects, and what Figma needs to prove on profitability as AI disruption looms over creative software.Topics covered:Why Figma's valuation is still rich despite the stock's declineFigma vs. Adobe, Microsoft, Wix, and the private design-tool fieldAI's disruption risk to creative/design softwareQ1 2026 results: $333M revenue, 139% net dollar retention, $89M free cash flow (27% margin)Free cash flow per share and the impact of IPO-related dilution$1.6B cash, no debt, and why M&A could be on the tableThe $116M RSU tax settlement behind the cash balance dipQ2 and full-year 2026 guidance (40% and 35% YoY growth)This episode is sponsored by fiscal.ai — get 15% off any paid plan at fiscal.ai/csi.Liked this breakdown? Head to chipstockinvestor.com for our full library of semiconductor and tech stock research, deep dives, and analysis — plus access to Semi Insider, our premium subscription for investors who want the data behind every call we make.

What does it look like when a designer with zero technical background commits to shipping something real?That's what this week's episode with Brett Williams is all about.He gives us a behind-the-scenes of his journey building Gather including:Brett's process for iterating on a designBrett's new prototyping.md skill workflowHow Brett maintains control while building with AIBrett's strategy for adding sound design to GatherWhen Brett relies on Figma vs. explores directly in codea lot more

What does it take to walk away from a decade in product, and a job most people would envy, to bet on yourself?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Peter Yang, who just left his product lead role at Roblox to go full-time on his newsletter and podcast, Behind the Craft and build his own projects. Peter talks through the trade-offs of solopreneur life, why his calendar is suddenly empty, and how he uses an AI personal advisor with three principles to decide what to say no to.They explore his day-to-day AI builder stack, from running Codex as a daily driver to using Hermes for his recurring scheduled tasks, his working definition of slop and why he guards against it, and what he's actually measuring as success now that nobody is handing him a promotion.If you're a PM weighing whether to leave a stable job to build on your own, a creator trying to scale output without sliding into slop, or anyone wiring AI agents into their daily 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

The Information's Elon Musk reporter Theo Wayt and Crypto reporter Yueqi Yang talk with TITV Host Akash Pasricha about SpaceX's historic public debut, retail investor allocations, and price discovery happening via crypto perpetual contracts. We also talk with TMF Associates President Tim Farrar about Starlink's dropping ARPU, terminal cost subsidies, and upcoming launch site expansions, and The Information's AI reporter Stephanie Palazzolo about how Anthropic is blindsiding key partners like Figma and Canva by moving directly into the software application layer.Articles discussed on this episode: https://www.theinformation.com/briefings/spacex-shares-open-150-per-sharehttps://www.theinformation.com/briefings/crypto-traders-bet-spacex-ipo-popping-20https://www.theinformation.com/articles/anthropic-blindsides-business-partnersSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - SpaceX Goes Public in Historic $2T IPO 10:06 - Starlink Margin Squeeze & Telco Threats 19:57 - Inside the SpaceX Retail Trading Playbook 33:54 - Anthropic Blindsides Software App Partners

Hoy hablamos de Anthropic metiéndose en terreno de Figma y Canva con Claude Design, Xiaomi liberando MiMo Code como agente coder con memoria persistente, Google negociando con Samsung para parte de sus próximas TPUs Icefish, la nueva fábrica europea de Infineon en Dresde y la tripulación de Artemis III.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

From the comical wizard prompts of 2024 to the heavy-lifting reality of Figma MCP, Davy and PJ chart the compressed timeline of design system and canvas automation.

What does it take to bring AI into businesses that run on physical work, human judgment, and processes nobody has ever written down?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Noah Levin, founder of Serious People, to unpack what he calls being a “free-range AI consultant.” Noah explains why most of his work is business consulting from first principles rather than AI consulting, why agents still need humans to deliver real value, and how he groups AI for any company into three buckets: a coworker, an operator, and a product or engineering capability.They explore how AI is collapsing the distance between a conversation and a working prototype, why the new IP is business judgment instead of code, why he believes everything is becoming product management, and the humility it takes to solve problems on a client's terms inside companies that aren't, and shouldn't be, run like tech startups.If you're a product leader figuring out where AI actually creates leverage, an operator weighing whether to go independent, or a builder realizing that distribution now matters more than the thing you build, 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

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 (00:00:48) How Wix Was Founded (00:23:46) Why Clients Keep Using Wix (00:26:19) How Much of Wix Is Actually Vulnerable to AI (00:35:32) Why Wix Is More Sticky Than It Seems (00:37:01) Whether Vibecoding Is Likely to Disrupt Drag-and-Drop Website Building (00:45:34) Why Base44 Could Change the Entire Investment Case (01:01:25) How Wix Could Survive and Turn Into a Multibagger (01:04:29) Valuation Discussion of Wix (01:09:21) Whether Shawn and Daniel Add Wix to the Intrinsic Value Portfolio Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. 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: ⁠Fiscal.AI⁠ References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

In this "quick one", Barry and Phil crack some beers and explore the impact of AI on design leadership, change management, and the future of work. They discuss recent developments in AI tools like Figma agent and Gemini, the cultural and emotional implications of AI adoption that come with this change, and practical strategies for integrating AI into creative and organizational processes, including the importance of focusing on value and experimentation in AI adoption. It is a timely and much-needed conversation for design leaders, and creatives in general, in the throes of AI transformation. Enjoy!Drinks: Sprindrift Blood Orange Tangerine Sparkling Water, Tree House Brewing Company Waffleberry Double IPALinks: www.figma.com

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

In today's conversation, Brett sits down with CMO of Figma, Sheila Joglekar Vashee. Previously the second marketing hire at Dropbox, where she helped scale the company past $1 billion in revenue, she now leads marketing at Figma fresh off its IPO. In an industry that has spent a decade trying to turn marketing into something closer to hedge fund trading, Sheila argues the art was always the point — we just stopped talking about it. She unpacks how to run marketing as a portfolio of moonshots, why giving teams different goals breeds dysfunction, how to scale taste across an organization, and why old playbooks are obsolete, even as the fundamentals hold. In today's episode, we discuss: How to run marketing like a portfolio of moonshots The value of disruptive energy for senior marketers Why "Ubiquity is the opposite of cool" How to actually scale taste across an organization What great marketing looks like in the AI era Referenced: Apple: https://www.apple.com/ Dennis Woodside: https://www.linkedin.com/in/dennis-woodside-341302/ Dropbox: https://www.dropbox.com/ Dylan Field: https://www.linkedin.com/in/dylanfield/ Figma: https://www.figma.com Francoise Brougher: https://www.linkedin.com/in/francoise-brougher-341a72/ Gap: https://www.gap.com/ Google Chrome: https://www.google.com/chrome/ Harley-Davidson: https://www.harley-davidson.com/ HubSpot: https://www.hubspot.com/ Notion: https://www.notion.com/ Opendoor: https://www.opendoor.com/ Pinterest: https://www.pinterest.com/ Square: https://squareup.com/ The Web Is What You Make of It (Dear Sophie): https://www.youtube.com/watch?v=pzOBOuyr-EU Urban Outfitters: https://www.urbanoutfitters.com/ Yamini Rangan: https://www.linkedin.com/in/yaminirangan/ Where to find Sheila: LinkedIn: https://www.linkedin.com/in/sheilavashee/ X: https://x.com/sheilavashee Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986644/ X: https://x.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 00:07 What excellent marketing actually is in 2026 01:36 Why giving teams different goals creates dysfunction 02:36 The most important decision Sheila made as CMO last year 04:26 The real difference between an SVP and a CMO 06:05 Marketing is one engine - not separate pieces 07:15 The tension between brand and growth 09:25 The decisions a CMO should never be making 09:55 Running marketing like a portfolio of moonshots 12:46 "Ubiquity is the opposite of cool" 15:11 Why a few companies get a flywheel of momentum 16:44 The Silicon Valley clock and irrational perception cycles 19:25 How to actually scale taste across an org 21:09 What changes for a CMO in a post-LLM world 23:15 Why the artistic side of marketing never really left 26:05 Whether taste can ever be encoded in software 27:15 Telling an optimistic, yet realistic story about AI 30:50 You need to make people care 32:11 What surprised Sheila about being a public-company CMO 33:46 Why Figma won enterprise where Dropbox couldn't 35:25 Sheila's favorite campaign ever 37:10 Why announcement videos full of humans, lack humanity 38:55 Playbooks are obselete, but the fundamentals are not 40:25 Why marketing in 2026 demands disruptive energy 41:54 How Sheila architects her week 48:55 Where corporate politics actually come from 53:55 "Sheila, are you going to change the world in this job?" 58:09 What's unique about the CMO and CEO relationship

The "SaaSpocalypse"—the panic that AI will make software-as-a-service obsolete—hasn't rattled Figma's Matt Colyer. As the company's director of product management for developers, he's been building his own agents for two years and is buying more software services than ever.In addition to making the case that AI is a “goldmine” for SaaS companies, Colyer talked with Dan Shipper for AI & I about why great design requires a diamond-shaped process: First you diverge, generating as many ideas as possible, then you converge around the best ones. Chat is linear, which makes it good for iterating on one design but bad at generating lots of options. Figma's new on-canvas agent is a first attempt at fixing that.They also get into why AI design tools need to break free of the text box, how Figma's MCP server is closing the loop between code and design, and why "review" has become the biggest bottleneck in AI-assisted product work.If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps:1:03 - Introduction2:15 - Why the SaaSpocalypse narrative has it backwards5:27 - Matt's email agent origin story13:21 - Divergent vs. convergent design thinking17:39 - Figma's MCP server19:45 - Why design agents need personalization22:09 - Every problem is a context problem25:12 - Apple and Google as the reigning kings of context28:18 - Why review is the new bottleneckLinks to resources mentioned in the episode:Matt Colyer on X: https://x.com/mcolyerFigma: https://figma.comFigma MCP server: https://www.figma.com/blog/introducing-figma-mcp-server/

DAMIONCarnival Corporation's data breach exposed personal data of nearly 6 million customers: An April social engineering attack on an employee account compromised names, dates of birth, and government-issued ID numbers. WHO DO YOU BLAMESkills: Technology & Cybersecurity: Experience with information technology and cybersecurity matters is increasingly important to mitigate the risks our business faces, promote innovation and maintain a competitive edge in a rapidly evolving technological ageLeast represented 5/11CEO Josh WeinsteinNO: at Carnival since 2002, started as General CounselSir Johathon BandNO: First Sea Lord and Chief of Naval Staff, the most senior officer position in the British Navy (2006 to 2009, when he retired); Admiral and Commander-in-Chief Fleet (2002 to 2006); Served as a naval officer in increasing positions of authority (1967 to 2002)Jason CahillyNO: CEO Dragon Group LLC, provides capital and business management consulting and advisory services worldwide; The NBA: CFO & Chief Strategic Officer; Goldman Sachs: Partner; Global Co-Head of Media and Telecommunications; Head of Principal Investing for Technology, Media & TelecommunicationsNelda ConnorsNO: CEO/Chair Pine Grove Holdings, a privately held investment company; CEO Atkore International, manufacturer of electrical, safety and infrastructure solutions; VP Eaton Corporation, electrical and automotive supplierLaura WeilNO: Founder Village Lane Advisory LLC, specializes in providing executive and strategic consulting services to retailers COO New York & Company, women's apparel and accessories retailer; CEO Ashley Stewart, women's apparel retailer; CEO Urban Brands, apparel retailer; COO AnnTaylor Stores, women's apparel retailer; CFO American Eagle Outfitters, apparel retailerAudit Committee: Oversee management's risk assessment processes to identify principal and emerging risks, including financial, IT, cybersecurity and non-HESS operational risksLaura Weil*: NOJason Cahilly: NOJeffrey Gearhart: NOWalmart Corporate Secretary and lawyerStuart Subotnick: NOCEO at Metromedia Company, wireless/communications, until 2010; Carnival director since 1987 Health, Environmental, Safety and Security Committee: Oversee management's processes to identify principal and emerging health, environmental, safety, security and sustainability-related risks, including those related to ship operations and cybersecurity, RAAS health, environmental, safety, security audits, IAG and external investigations into significant ship incidents, and health, environmental, safety, security-related hotline complaints, and assess the steps management has taken to minimize such risks.Sir Johathon Band*: NONelda Connors: NOHelen Deeble: NOFormer CEO P&O Ferries Division Holdings, shipping and logistics businessKatie Lahey: NOExecutive Chair Korn Ferry Australasia, leadership and talent firmMicky Arison (75%): Exec Chair and former CEO and 7% stockholderThe CEO Pay Ratio1,063:124 retail CEOs made as much in a day as their typical employee earned in a year — and a big one didn't. WHO DO YOU BLAMEThe separation of CEO and Chair: Hamilton E. James Chair/Ron Vachris MMNot uniqueOnly 50% of the board is men. WTF?uniqueOne share = one voteNot uniqueState of HQ = WashingtonAlso StarbucksState of Inc = WashingtonAlso StarbucksPledge of allegiance to stakeholdersCostco generally has: Higher wages; Better benefits; Lower turnover; Higher sales per employee.Industry-leading employee compensation AND Self-imposed low-margin pricing philosophyWalmart only low-margin pricingOther comps:Todd Vasos of Dollar General, Shane O'Kelly of AutoZone, Gerald Morgan of Texas Roadhouse, Jack Sinclair of Sprouts Farmers Market, William Stengel of Genuine Parts Company, Michael Creedon of Dollar Tree, Ronald Sargent of Kroger, Lauren Hobart of Dick's Sporting Goods, Joshua Kobza of Restaurant Brands Inc., Kecia Steelman of Ulta Beauty, Scott Boatwright of Chipotle, Ted Decker of Home Depot, Bob Eddy of BJ's Wholesale Club, Corie Barry of Best Buy, James Conroy of Ross Stores, Chris Turner and David Gibbs of Yum Brands, Chris Kempczinski of McDonald's, Marvin Ellison of Lowe's, Brian Cornell of Target, Ernie Herrman of TJX Companies, Doug McMillon of Walmart, Brian Niccol of Starbucks, Hal Lawton of Tractor Supply Co, Laura Alber of Williams-SonomaFigma Gets an Activist Investor. Exhibit A on Why Companies Don't Want to Go Public. Figma's first year as a public company hasn't gone well. Findell Capital Management said it needs to take steps to shed its unwarranted reputation as an artificial-intelligence “loser.” WHO DO YOU BLAME?Figma founder and CEO Dylan Field: Owns 10% of shares but 72% of voting power: Class B shares worth 15 votes per shareDylan owns 158 Class A Shares (or 0.00003556% of 444,278,887)And Chair$5B net worth$865M total summary compensation in 2025; $91M in 2024Nominating Agreement:Figma must nominate Dylan Field to be a director and include him in the proxy statementThe company must use its resources to back him up and actively convince other shareholders to vote for him In response to a question about how he was going to change the world, Dylan said he was going to build better software for drones.Bro fest sausage party2 of 9 directors are womenTop 5 NEOs all dudesPeter ThielForced Dylan to drop out of Brown for a dumb fellowshipVC Blowhardiness on the BoardVC dude John Lilly (Greylock): Lead Independent Director2nd longest tenure (2014)Member of the Audit Committee; Member of the Nominating Committee (only Lilly and Rimer)VC dude Andrew Reed (Sequoia)Director at debt-maker Klarna Group (also way down since IPO): down roughly 54% from its initial $40.00 IPO price, and down nearly 68% from its all-time highMember of the Compensation Committee (which modeled Dylan's pay package after Elon Musk)VC dude Danny Rimer (Index Ventures)Director since 2014B.A. in History and Literature from HarvardMember of the Compensation Committee (which modeled Dylan's pay package after Elon Musk)Member of the Nominating Committee (only Lilly and Rimer)Luis von AhnDuolingo co-founder and CEO2025: shared an internal email outlining Duolingo's new "AI-first" strategy where Duolingo would “gradually stop using contractors to do work that AI can handle”Stated that "AI is a better teacher than humans" and that the future role of teachers would be reduced to providing "childcare."Blamed the controversy on a "lack of context" in his original statements"AI-First" memo goes viral: $389; today $118MATTDanone, Starbucks shine in methane-reduction rankingDanone is the only company in the group aligned with the Global Methane Pledge, an initiative backed by 150 countries that targets a 30 percent reduction in global levels of the gas by 2030. The French multinational also leads the pack in progress toward its target, having come close to hitting it five years ahead of schedule.WHO DO YOU CREDIT?Chair of the CSR committee Lise Kingo (9% influence), one of three directors tagged as merit directorsmaster's degree in Responsibility & Business from the University of Bathbachelor degrees in Religions and Ancient Greek Artbachelor's degree in Marketing and Economicscertificate as International Director from INSEADEx Novo Nordisk environmental affairs, internal audit, compliance, human resources, communication, branding and sustainabilityHelped create the UN SDGs and the UN Global CompactSomehow only bats 559 on carbon intensity (career) and 415 for scope 1/2 (career)Also, using deference metrics, the ONLY DIRECTOR tagged as fully independentEmployee rep member of the CSR committee Bettina Theissig (5% influence) and the employees of DanoneThe committee charter mandates employees get a say: At least two thirds of the CSR Committee must be independent, as defined by the AFEP-MEDEF Code. At least one Director representing employees must be a member of the Committee.In France (Danone's domicile), the European Investment Bank found that French employees were the most aware of environmental issues - 82% of French employees said they were highly concerned about environmental issues, highest in EuropeLead Independent Director and chair of the Nom/comp committee who put together the comp plan, Valerie Chapoulaud-Floquet15% influence, second to the 18% influence CEO (democracy!!), got 99.16% shareholder approval in April (even as CEO got 89.73% approval and pay got 93.19% approval)20% of short-term pay and 30% of long-term pay is based on hitting sustainability targetsWhen you pay a CEO to do a thing, they are more likely to do a thingEx-CEO Emmanuel FaberOusted in 2021 by the board of directors and activist investors, he transformed Danone into an “enterprise a mission” (a French version of a B corp)Investors voted 99% in favor of the move and a year later ousted Faber, the board resigned, and the new board and CEO are basically moving back towards being environmental leaders because it paid offShort term share price laggedHe said in 2024 that nature is “at the core” of Danone, It took the stock 3 years from Faber's ousting to return to Faber levels - and in the meantime, they were sued for plastics and emissionsIsn't this HIS win?Current CEO Antoine de Saint-AffriqueBecause CEOGM Board Director Jonathan McNeill Stepping DownCEO of DVx Ventures. Ex COO at Lyft Inc. and ex president, Global Sales, Delivery and Service at Tesla, current director at Lululemon, GM director since 2022, on the Governance and Corporate Responsibility committee and Risk and Cybersecurity committee.We know that half of boards on average think someone on the board should be replaced - did the GM board not like McNeill?WHO/WHAT WOULD WE BLAME FOR PUSHING MCNEILL OUT?Outsider dude bro DRLet's be honest, McNeill worked at much more… modern?... companies than GMThe board is OLD SCHOOL - ex Northrop Grumman, ex Visa, ex Lazard, ex HP, ex eBay, ex Novartis, ex Walmart, other directorships at Goldman, Huntsman, P&G… these are professional, insular boardsMeanwhile, he's investing as a VC in AI, other auto/mobility startups, comes from boards that are bro founder lead (Tesla, Lyft) He's invested in AI, crypto, heavy tech, intertwined with VCs all overNot deferential enoughBarra is connected to 94% - THE ENTIRE - boardMcNeill has the highest network power on the board at $9tn, higher than even Mary Barra (who is super connected), but is NOT a power player in the board community of GM - the dominant board communities for GM are massive blue chip US companies, where McNeill has deeper connections in smaller IT/tech focused companiesHe doesn't need the pay, he gets nothing for the connections really, he has connection to Barra but his network is different - was he too independent?Pissed he doesn't have enough influence McNeill has the LOWEST influence on the GM board at 4%He's relatively new, younger, working as a VC where you have a lot of power of capital allocation“I don't need this shit” effect?Too many womenMcNeill's dvX ventures portfolio team is 6 dudes and 1 womendvX entire operations staff is two woman - guess what they do“Chief of Staff” (ie, HR)Executive Assistant (yes, listed on the team)Board is 2 women, 3 men (McNeill not on board)This one seems unlikely I guess?Too busy, meh, move onOne of dvX portfolio companies is curbee, with GM Ventures' Kurt Baumgarten on the board (and the dvX co-founder is founder of Curbee)McNeill on at least 3 of his portfolio boards or advisory committees, plus LULU and GM…

Is per-seat pricing dying a slow death, and is your SaaS expense structure ready for its replacement? In episode #373, Ben Murray breaks down the shift from per-seat subscriptions to usage and outcome-based pricing, and what it means for your finance org. Bloomberg projects subscription pricing falling from 60% to 30% of SaaS models over the next decade, while outcome-based pricing climbs from 10% to 60%. This is no longer a thesis on a slide. GitHub, Salesforce, Zendesk, Intercom, Figma, HubSpot, and others are already repricing, and public companies are reporting AI ARR in the hundreds of millions. If you cannot answer what your AI margins are when the board asks, you are already behind. See exactly how legacy SaaS leaders are repricing, from Zendesk charging per automated resolution to Salesforce billing per AI conversation plus flex credits, and what GitHub's June 1 move to token-based billing signals for the rest of the market. Understand why a single bucket of cloud hosting that blends traditional infrastructure with inference spend leaves you blind, and what instrumentation to put in place before budget season. Learn the questions your board will ask about AI margins, and how to answer whether low-usage customers are quietly subsidizing your heaviest users. Get the case for reconvening your pricing committee now to align product roadmap, AI features, and the expense framework that tracks them. Know which AI unit economics to track by revenue stream and by usage bucket so you can defend margin as your pricing model changes in real time. Listen now and put the tracking framework in place before the AI margin questions land on your desk. Resources Mentioned Ben's blog post: https://www.thesaascfo.com/saas-per-seat-pricing/ New course on AI unit economics and metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas

n this special segment of The Full Ratchet, the following Investors are featured: David Ulevitch of Andreessen Horowitz Jake Saper of Emergence Capital Sandesh Patnam of Premji Invest Each investor highlights a situation where they decided not to invest, why they passed, and how it played out. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

In this episode, we debrief Telehash #4 and dig into the open-source future of Bitcoin mining. We share behind-the-scenes metrics from HydraPool's six-and-a-half–hour live stress test, including 30.8 zettahashes processed, an average of 1.32 EH/s, a peak of 2.495 EH/s, 2,231 workers, 59 unique users, and an impressively low ~1% server CPU under >2,000 connections. We explain why rejection rates under ~2% matter, how stale and “difficulty too low” shares differ in solo vs pooled mining, and how Stratum “suggest difficulty,” plus our d= and h= password parameters, help right-size starting difficulty—making Telehash inclusive for both exahash renters and single-chip Bitaxe miners. We also touch on leaderboards, loyalty uptime rules, and shout out supporters like Elektron Energy, Compass, Saaz Mining, and Abundant Minds. From hardware to policy, we discuss Bitaxe UX updates (LVGL, Figma-driven UI, external display/knob), DOOMAXE fun, and industry standardization—from firmware and pools to racks, cooling, and power—arguing that open reference designs cut costs and risk for everyone. We cover GridPool's “winners list” approach to decentralized variance smoothing, the Patoshi/extra nonce story, vardiff dynamics, and privacy-conscious VPN mining. We reflect on immersion's decline versus hydro, ASIC roadmap realities and slowing efficiency gains, the supply-chain and security stakes (FCC Wi‑Fi moves, vendor backdoors), and why nonprofit coordination via the 256 Foundation matters for open firmware, dev kits, and reference designs. We close with community invites, next steps for Telehash #5, and a call for ASIC makers and big miners to collaborate on open standards that benefit small and large operators alike.

In der heutigen Folge sprechen die Finanzjournalisten Nando Sommerfeldt und Holger Zschäpitz über florierende Luftfahrt-Titel, darbende Chemie-Werte und den historischen Ferrari-Moment. Außerdem geht es um Lufthansa, Air France-KLM, MTU Aero Engines, Ryanair, TUI, BASF, Brenntag, Deutsche Börse, Delivery Hero, Uber, Prosus, Ferrari, Porsche, Klarna, StubHub, Chime, Figma, CoreWeave, Circle, Cerebras, Fervo Energy, HawkEye 360, Aramco, Alibaba, SoftBank, NTT, Visa, AIA, Enel, Meta, General Motors, ICBC, Bank of America, Goldman Sachs, DoorDash, Entain, Scout24, Big Yellow Group, Melrose Industries, Argenx, Alpha Bank, UniCredit, Commerzbank. 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 Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

⭐️ Check out Andy's course - Craft at Speed https://join.dive.club/maven-aff-andy-madrickUse code DIVECLUB for 15% offAndy Madrick ( https://x.com/andymadrick ) is rethinking what it means to be a designer in 2026.While at Notion, he's developed a workflow where he prototypes directly in code, ships frontend PRs, and uses AI tools like Cursor to own the final polish of user interfaces.In this episode we get a look into how he keeps all these things in motion and still designs with insane attention to detail.Some highlights:Building one of Notion's most visible UIsWhy recreating interactions teaches you craftOwning the last 5-20% of frontend polish workThe “jelly bean factory” problem with AI outputWhen to start in Figma vs. jumping straight to codeHow Andy creates prototypes that content teams can edita lot more

Is Figma holding design systems back?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.It's the month before Config! So we're diving into a spicy topic - is Figma helping or hindering design systems? Is the future of design systems a world where Figma doesn't exist? Or are design tools like Figma essential for the work we do in design systems day to day?Show notesFigma Weave (acquisition / launch)Figma Make Kits announcementThe State of AI in Design Systems (zeroheight)Design Systems Report 2025 (zeroheight)UX Tools Design Tools SurveyConverge (zeroheight)Luke Murphy on BlueskyMichelle Chin on Bluesky

Episode web page: https://bit.ly/42TFjM4 Episode summary: In this episode of Insights Unlocked, Manú Bartlett speaks with Pedro Hernandez, advocacy manager for EMEA and Latin America at Figma, about how AI is reshaping design, leadership, and the future of creative work. Drawing from more than 15 years of experience across UX, product design, and design operations, Pedro shares why the industry is moving beyond a narrow focus on speed and toward a deeper balance between experimentation, craft, and human-centered thinking. Pedro explores how design communities across Europe and Latin America are adapting to AI in different ways, why leaders must rethink how they evaluate design work in the age of rapid prototyping, and how “craft” now means being intentional, critical, and thoughtful when collaborating with AI tools. He also discusses the growing importance of soft skills, curiosity, and research as teams become more multidisciplinary again. The conversation also dives into how leaders can stay connected to customer needs while navigating AI-driven workflows, why experimentation matters at every level of an organization, and how Figma is researching what design leaders truly need from modern collaboration tools. You'll learn: How AI is changing the relationship between speed, experimentation, and design craft Why the best design leaders are learning to balance rapid production with intentional decision-making What “craft” means in the era of AI-powered design workflows How Latin American and European design communities are approaching AI differently Why curiosity, research, and soft skills are becoming more important in modern product teams How leaders can stay closer to customers while adopting new AI tools and workflows Why the future of design may look more multidisciplinary and collaborative again Resources & links Pedro Hernandez on LinkedIn (https://www.linkedin.com/in/pedrohernandez/) Figma's Config (https://config.figma.com/) Figma's State of Design report (https://www.figma.com/state-of-design/) Manú Bartlett on LinkedIn (https://www.linkedin.com/in/manubartlett/) Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/) Learn more about Insights Unlocked: https://www.usertesting.com/podcast

What if the thing holding you back from posting isn't laziness or a lack of ideas, it's that nobody ever told you the cringe feeling goes away, and what it actually looks like to build trust with an audience without burning it?In this special episode of Supra Insider, Ben Erez sits down with Hilary Gridley, creator of the Maven course “How to Become a Supermanager with AI,” and Mallory Contois, former VP of Growth at Maven and founder of The Old Girls Club, for a candid conversation about self-promotion, audience building, and the surprisingly practical mechanics of showing up consistently online without losing yourself in the process. Hilary published a guest post on Lenny's Newsletter the same morning this was recorded, and that pipeline, from writing to course to full-time career, is exactly what the conversation unpacks.They cover why trust is the most durable professional asset you can build, how to think about the value exchange between creator and audience, why the psychology of “doing work in private and handing in the final product” makes content creation feel so unnatural, and what both of them actually do to stay consistent without spiraling into algorithm-chasing. Mallory drops a deceptively simple Apple Notes system for never running out of ideas. Hilary makes a sharp case for starting with a talk instead of a newsletter. And both of them are refreshingly honest about the fact that posting still feels mortifying sometimes, and why you should do it anyway.If you're a PM, operator, or founder who has been sitting on the sidelines of content creation because it feels cringe, trying to figure out which platform to start with, or building an audience and wondering how to grow it without compromising the thing that makes your voice worth following- 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

Google I/O 2026 happened. And a few things they announced will actually change how you work as a designer. In this episode I break down the 7 that matter most — no fluff, just the stuff that's relevant for UX and product designers right now.  IN THIS EPISODE → Google Stitch — the free AI design tool that made Figma's stock drop 9% in one day→ Material 3 Expressive — Android's biggest redesign in a decade, now production default→ Google Pics — finally an AI image tool where you can change one thing without regenerating everything→ Google Flow + Veo 3 — AI video that's actually useful for design content→ Android XR Glasses — shipping this fall, and the design conventions are still being written→ NotebookLM + Workspace Studio — automated UX research workflows that actually work→ Agentic UX — why designing for screens is no longer enough  Resources:Google Stitch → stitch.withgoogle.comMaterial 3 Figma Kit → figma.com/community/file/1035203688168086460Google Flow + Veo 3 → labs.google/fx/tools/flowVeo 3 in AI Studio → aistudio.google.com/models/veo-3Android XR Design Docs → developer.android.com/design/ui/xrNotebookLM → notebooklm.google.comWorkspace Studio → studio.workspace.google.comAll I/O 2026 announcements → blog.google/innovation-and-ai/technology/developers-tools/google-io-2026-collection/AI for Designers: 5-week Bootcamp

Web funnels are like teenage hex.Everyone claims they are crushing it. Almost nobody really has a clue.Elise Zareie spent last year actually building them.She has been in UA since 2019. Last year she became a part-time product manager just to ship funnels herself. That meant learning Figma, coordinating designers, front-end developers, back-end developers, and analytics teams. Running QA. Shipping it. Then using AI to test faster than she ever could before.In this episode she talks through what the process actually involves, the three levers that move the needle in any funnel, the one benchmark she watches obsessively on landing pages, how she uses Claude to generate full funnel copy from screenshots of top-performing creatives, and why AI visuals are making consumers more suspicious, not less.Key topicsWhy web funnels are harder to build than most people think and what the process actually looks likeHow to identify the dominant funnel in a vertical before building anythingThe 40% page-one to page-two benchmark and what to do when you fall below itHow to match landing page copy to ad creative using UTM tagsHow Elise uses Claude to generate funnel copy and assessment questions from top-performing creativesHow AI helped her launch a male-specific funnel in two weeks for an app with 80% female usersWhy AI-generated visuals are creating consumer suspicion and what to do about it

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

What does it actually mean to bet on yourself, and how do you know if the game you're playing is really the one you want to be in?In this special episode of Supra Insider, Marc Baselga and Ben Erez record together in person for the first time, sitting down at a studio in New York to have an honest, unscripted conversation about optionality, partnership, and what they've learned from building Insider Loops over the last seven months. They open with the question hanging over a lot of high-agency people right now: with AI making it easier than ever to go from idea to product, should you leave your job and bet on yourself? Marc names the only full-time role that genuinely tempts him, Anthropic, and then explains exactly why he still wouldn't take it. Ben unpacks why he accomplished 20% of what he was capable of during his full-time years, and what changed.They go deep on why bootstrapping is harder than raising VC money, but why the constraints force the kind of market discipline that most funded companies never develop. They map out their complementary skill sets, how they've shifted from long-term planning to weekly cadence, and why they now think planning more than a week ahead is mostly a waste of time. The conversation closes on the role of the podcast itself, why it has to stay separate from the business, why fun is an emergent property and not a frivolous goal, and why the relationship comes first.If you're weighing whether to leave a stable job and go off on your own, curious about what a bootstrapped partnership actually looks like day to day and what makes it work, or just want a rare honest conversation between two builders about what they'd do differently and what they wouldn't change, this episode is for you.A special thanks Alex Pavlou and the team at 28th & Park for the recording space!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

2,5% Zinsen p.a. auf ein unbegrenztes Guthaben mit bis zu fünfmal der gesetzlichen Einlagensicherung*. Auch für Kinder. Das gibt's bei Scalable Capital. Mehr Infos hier: https://de.scalable.capital/tagesgeld?utm_medium=affiliate&utm_source=qualityclick&utm_campaign=broker&utm_term=655&c_id=QC5a486e706d75r687A578577c4a5a406d7364766d717e557847 Zinssorgen drücken Tech, Gold und Krypto. Alphabet platziert Rekord-Anleihe in Japan. Technoprobe explodiert 30%. Magnum Ice lockt Private Equity. Figma trotzt KI-Konkurrenz. Mercedes liebäugelt mit Rüstung. Essilor Luxottica (WKN: 863195) verliert 40% seit Jahresstart. Ein Erbstreit in der Gründerfamilie belastet, Smart-Glasses boomen, aber die Konkurrenz von Google, Apple und Samsung wächst. KGV bei 23. Buffetts Lunch gibt's dieses Jahr für 9 Mio. $ statt 19 Mio. $. Berkshire Hathaway (WKN: A0YJQ2) kauft Delta Air Lines, stockt Alphabet massiv auf und verkauft Visa, Mastercard, Amazon und mehr. Diesen Podcast vom 18.05.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. *Veränderlicher Zins auf unbegrenztes Guthaben. Konditionen sowie Guthabenverteilung auf scalable.capital/tagesgeld. Learn more about your ad choices. Visit megaphone.fm/adchoices

NLW previews Google I/O and the bigger question hanging over it: whether Google can turn its massive AI advantages into products people actually want to use. The episode connects Codex coming to ChatGPT mobile, the rise of always-on agents, rumors around Gemini Spark, and Google's potential opening as a cheaper high-performance model provider for builders and enterprises. In the headlines: Cerebras' explosive IPO debut, Figma's AI recovery, OpenAI and Apple tensions, Anthropic's massive new valuation, and more.Apply for our Growth Engineering role: ⁠⁠⁠https://jobs.aidailybrief.ai/⁠⁠⁠Enterprise Claw Cohort 3 Registration: ⁠https://enterpriseclaw.ai/⁠Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/Navigate⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Granola - The AI notepad for people in back-to-back meetings. 100% off your first 3 months with code AIDAILY at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://granola.ai/aidaily⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠https://scrunch.com/⁠⁠⁠Mercury - Modern banking for business and now personal accounts. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://mercury.com/personal-banking⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Drata - The agentic trust management platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://drata.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

We sat down with Dan Gent, who ran his product design agency, Lighthouse, for 15 years before walking away in 2023.Dan details how Lighthouse reached 20 people and roughly $2 million in revenue before market conditions forced a closure rather than a traditional exit. He explains the dangerous "dip" where profit margins drop as you hire non-billable managers and ops staff to scale. This conversation is a blunt look at the reality that businesses often end by closing or walking away instead of a multi-million dollar payout.We also tackle the difficulty of design retainers and why clients often stop seeing value once the initial project is finished. Dan advises treating yourself as an employee with a set wage in your forecast so you can actually afford to hire your replacement. He also touches on how AI is commoditizing "grunt work," forcing designers to sell accountability and taste instead of just Figma files.-----MENTIONS IN THE EPISODE:Follow Dan:LinkedIn: https://www.linkedin.com/in/dangent/The Dear Agency Founder Newsletter:https://join.dearagencyfounder.com/?utm_source=agencygrowthpodcast-----RESOURCES:Want the tools and resources we recommend for agencies? Check them out here:https://www.agencygrowthpod.com/tools-----NEWSLETTERWant the show in your inbox? Sign up for the newsletter!https://www.agencygrowthpod.com/newsletter-----COMMUNITYLooking to join a community of agency owners? Join our Discord!https://discord.gg/uvHRRRFVRD-----CONTACTGot something to say? Send us a message:https://www.agencygrowthpod.com/contact

Market update for Friday May 15, 2026Check out the Public app for incredible investing tools and to support the show (LINK)Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode, Zaid covers:Why oil prices are rising after the Trump-Xi summit OpenAI preparing possible legal action against Apple over their failed ChatGPT partnershipHow Ford became an AI stock after launching Ford EnergyFigma crushing earnings and proving software stocks aren't dead yetApplied Materials posting record results but the stock falling anywayPlus, Zaid answers a listener question about his journey from civil engineering to hosting The Rundown

In der heutigen Folge sprechen die Finanzjournalisten Philipp Vetter und Holger Zschäpitz über neue Rekordmarken an der Wall Street, den Traumstart des Nvidia-Jägers und Photonics-Phantasie bei POET Technologies. Außerdem geht es um Nvidia, POET Technologies, Cerebras, Goldman Sachs, Cisco, StubHub, CTS Eventim, Ford, Coinbase, Robinhood, Applied Materials, Figma, Ondas, Palantir, Infineon, Siemens, SMA Solar, Fraport, Borussia Dortmund, Biontech, Tesla, Xiaomi, Apple, Micron, Blackstone, Boeing, TSMC, Sandisk, Intel, Lumentum, Seagate, Western Digital, Ciena, Coherent, AMD, Rackspace Technology, MaxLinear, Agilon Health, Bandwidth, Aehr Test Systems, Entravision, DigitalOcean, SELLAS, Bloom Energy, Atomera, Intuitive Machines, Arteris, Vicor, SiTime, TEQ - General Artificial Intelligence UCITS ETF (WKN: A41AXG). 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 Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

In this latest episode of Executive Function, Brett sits down with Praveer Melwani, CFO at Figma. Praveer joined Figma in 2017 as the company's first business operations and finance hire—when the team was around 30 people and not yet charging for the product—and stepped into the CFO seat in 2022, helping to lead the company's IPO in 2025. In today's conversation, Praveer breaks down the step functions that took him from IC to CFO, why Figma started acting like a public company three years before IPO, and how AI is rewriting capital allocation and the CFO job itself. In today's episode, we discuss: What separates a world-class finance leader from a traffic-cop CFO How Praveer went from Figma's first biz ops hire to CFO of a public company in nine years Why Figma started acting like a public company three years before its IPO What Praveer has learned working alongside Dylan Field for nine years Why Figma intentionally cut its 90% gross margin to invest in AI References: Adobe: https://www.adobe.com Brendan Mulligan: https://www.linkedin.com/in/brendanmulligan Cloudflare: https://www.cloudflare.com Dropbox: https://www.dropbox.com Dylan Field: https://www.linkedin.com/in/dylanfield/ Fidelity: https://www.fidelity.com Figma: https://www.figma.com GIC: https://www.gic.com.sg NerdWallet: https://www.nerdwallet.com Shaunt Voskanian: https://www.linkedin.com/in/shauntvoskanian/ Where to find Praveer: LinkedIn: https://www.linkedin.com/in/praveer-melwani Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 02:13 From banking to Dropbox to Figma 04:14 The phase shift when Figma's COO left 05:36 Hiring leaders in functions you don't understand 07:18 Selling the exec team on AI consumption pricing 09:48 Using Claude Code to learn new things as CFO 11:36 Building an internal board of peer CFOs 13:52 Inside Figma's CFO job description 16:38 What separates good CFOs from world-class CFOs 18:42 Capital allocation and risk in a post-ChatGPT world 21:45 Why Praveer wants to take more bets 24:32 How AI is materially changing the CFO role 25:36 The nine-year working relationship with Dylan Field 29:12 How deeply in the details should a CFO be? 31:47 What Dropbox taught Praveer about building strong teams 33:24 Praveer's first-principles test for hiring VPs 38:47 Why Figma acted like a public company in 2022

这期 Bear 带来了最近几件真实发生的事，串起来一看，都指向同一个问题：在 AI 让一切变得更快、更容易之后，我们到底在追什么？---**

AI for Designers: 5-week Bootcamp

https://youtu.be/aQyHwoGfy50 Max Kryzhanovskiy, President and CEO of MOS Creative, is driven by a desire to set an example for his children and show what's possible through technology, persistence, and innovation. As the leader of a tech-forward agency that builds websites, apps, and AI-enabled platforms, Max helps businesses move from idea to execution by creating digital products that solve real problems and scale over time. We explore Max's MVP Framework — Define the problem, Determine target market, Prototype the product, Build the MVP, Test and obtain feedback, Iterate — a practical approach for transforming ideas into scalable digital products. Max explains why founders should avoid overbuilding too early, how AI is accelerating prototyping and development, and why businesses must balance automation with authentic human connection. — Drive Growth Using AI Agents with Max Kryzhanovskiy  Good day, dear listeners. Steve Preda here with the Management Blueprint Podcast, and my guest today is Max Kryzhanovskiy, the President and CEO of MOS Creative, a company that builds websites and apps that drive growth. They were also the first company in Baltimore to launch a mobile site. Welcome to the show, Max.  Thank you for having me.  Let me ask you this—what is a mobile site? Is it a mobile phone site, or is it something different?  I mean, now it probably doesn't matter as much anymore, because everybody obviously has a website that works on a smartphone screen—or a responsive websites. But before mobile websites came out—or I should say, when smartphones first came out—we had to adjust for smaller screens. We were all used to bigger screens on a computer, and then once we started having different screen sizes come out before responsive, we were the first company to have a mobile website in Baltimore. And we actually built a web application specifically to create them ourselves, and then also went to market to offer it to other clients as well. So a mobile website is just like it sounds, a website that’s specifically designed for mobile.  That’s cool. So it sounds like you are very much a tech-forward company, and you are at the edge of technology. And as we were logging on, you said that you would be recording this on your phone because you actually have AI agents running on your computer. Does that mean you have AI agents as part of your team? What kind of agents do you have? Is it still an experiment, or is it already in execution mode?  It's in execution mode, but we're always experimenting. We like to think we're ahead of the curve, but with AI, we're all experimenting to a certain extent, right? Something new comes out, we try it out, see if it works, and see how it can be applied to your business—what kind of outcomes it can give you. So I'm all about AI. It's amazing. It's an amazing tool. But I think AI is becoming a lot more than we thought it was going to be—and also a lot less at the same time. Meaning, when AI launched—for example, when ChatGPT came out to the broader market—I mean, obviously AI had been around for a while—but when ChatGPT launched its chatbot platform publicly, we were amazed by how much work it could done. So it went from zero to a hundred. “Oh my God, it can do all of this,” right? But now, for example, with the more recent models—4.5, 5.0—the improvements are much smaller.  It's not a hundred percent or a thousand percent better anymore. Now it's maybe five or ten percent better, but the cost keeps increasing. I just read somewhere that even Claude said Claude Code won't be included much longer as part of the regular plan. So now it's only in the $200 higher-tier plan, plus you have to buy additional tokens. So it's really becoming more like, “Hey, yeah, we can do this for you—but you're going to end up paying something similar to what you'd pay a team.” At first, it was more like, “Let's get into the market. Let's get a lot of people interested.” But now, obviously, they have a lot of money behind them—investors, VCs, public market pressure—and they need to bring in revenue. So I think things are going to change very soon. AI is going to become a lot more expensive because the infrastructure and resources it requires are expensive. So eventually, those costs are going to be passed on to users. Yeah. And I noticed that ChatGPT started to do some ads as well. They’re probably going to go that direction, and who knows what that’s going to bring. But that's not our topic today. Today, it's about something else—frameworks. But before I go to the framework question, I'd like to ask you: what is your personal “why,” and how are you manifesting it at MOS Creative? Well, I'm a family man, so my “why” is to see my kids grow up to be amazing human beings—and hopefully to show them a great example of what can be accomplished in sports and in business. So my “why” is also to be a good person. Success can mean different things to different people, but for me, I love the hunt to get to a certain level of success. And then it's kind of like—us as humans, or at least a lot of people—we reach a certain level of success and we don't really celebrate it. It's more like, “Okay, let's get to the next level.” So my “why” is to show my kids that anything is possible if they really want it. Why I got into this space—it was exciting. You could see how quickly technology was moving, the kind of innovation that was possible, and it excited me. So that was one of the main reasons I got into technology. But the other reason was because I was in a different business, and we created technology that helped us grow. And I thought, “Oh wow, this is a completely different way to scale a business.” So technology became the direction we took. Yeah, I love it. I think inspiring our kids is a huge driver for many people, and it totally makes sense. Technology is exciting. I'd like to switch gears here and ask my other common question on this podcast, because this podcast is all about frameworks—business frameworks—how we can help listeners understand things, simplify things, and see different perspectives. So my question to you is: what is your favorite shortcut to success—or framework? And I don't mean “shortcut” in a negative sense, but rather a framework that allows you to understand things differently, make decisions, serve clients, and create valuable outcomes. Whatever it is—something that has worked for you, and is simple enough that you can explain it to listeners in three to five steps. Well, I believe in always being open to learning. It's not specifically a framework—it's more of a mindset: understanding that we don't know everything, especially now, with how quickly things are changing. I mean, a lot of people say that AI is going to make humanity a little dumber than we are. But actually, I learn a lot from it as well. If I'm doing something and I think, “Oh, this is a great way to speed up the process,” then I use it. So let's say, for example, a client asks me a question. There are different ways to approach it. If I already know the answer because I have specific experience with it, I can answer it, right? That doesn't always mean the answer is going to be correct.  I can research it, or I can get an answer from AI and then verify it through research and experience to make sure the outcome is actually what it says it's going to be. The learning part is making sure you're always open to figuring out whether the steps you've taken before are the right steps—or whether they can be optimized. I'm a big believer that everything can be optimized, especially now. There's almost no question that can't be answered quickly. Maybe there are some deep philosophical questions—but for the most part, especially in business, work, or even life, you can get answers very quickly. For example, I had a kind of vertigo-type feeling, and I was wondering what exactly it was. I entered specific prompts into ChatGPT, and it actually broke things down really well for me. Then I went to a doctor. First, I checked with a friend of mine who's a nurse, and she said, “This is probably what you have.” And she started asking me questions. I thought, “This is funny—these are exactly the same questions ChatGPT asked me.” And her husband said, “You know what? That proves that medicine is basically a set of questions. As you answer one question, it leads to the next.” So it's like a dynamic questionnaire. And by the time I got to the doctor, I already had a good idea of what it potentially was, and I knew what questions to ask so I could understand the next steps to fix it.  Yeah.  So what I'm saying is there’s always a way to improve. I'm a big believer in that. It doesn't matter what you're doing, because in this age, everything moves very fast—regardless of the business you're in. That's true. It's interesting that you say ChatGPT can answer any question. It's true—sometimes it hallucinates, but it still gives you an answer. Yesterday, I went to a presentation, and the president of Great Game of Business talked about this. He said, “Today, the answer is everywhere. So it's not a lack of answers—it's a lack of good questions.” So what we really have to come up with are good questions to ask. That's the bigger challenge now—not finding the answer. And I thought that was a really interesting insight. I agree. It's the same thing, right? It relates to prompts as well. If you have a good prompt, you're going to get a better answer. If you ask a good question, you're going to get a better answer. So yeah, I agree with you. Listen, AI isn't a complete solution, but it's a huge help—especially if you're just starting out. Yeah. So what drives your business? Is it technology? Is it trends? Is it something else? What drives it?  It's kind of a mix between technology and growth marketing. What that means is we work with clients all the way from ideation to scaling. We've also had several clients successfully exit. So clients come to us and say, “I have an idea. How do I take it to the next step?” Obviously now, there are AI builders and AI platforms that can help take a high-level idea and turn it into some kind of prototype—or at least a basic flow. But ideally, we work with clients from the idea stage all the way through design, development, launch, and driving traffic to the product. So the perfect client fits into that category. They might have an idea for a web application, mobile application, or software product.  They come to us and they're not really sure what the next steps are—or they've done some research For example, I spoke to a prospective client the other day. She worked with a developer who tried to build the product using an AI builder. For some reason, something didn't work out, and now she's back at square one. So now we have to review what she actually wants to build, determine the best approach, and figure out what phase one, phase two, and phase three should look like. So that's kind of how we work. For our clients, it's not just, “Let us develop it for you.” It's also about the creative side, the messaging, and the user experience. It's about making sure that when someone downloads the app—or visits the website or web application—it serves its purpose. It's a problem-solving product. It needs to solve a problem so users keep coming back again and again. And then we help grow it to new audiences. That's when it starts to scale and become exponential. Does that make sense? Yeah. So I’m wondering, you work from the idea forward, or you work from the outcome backwards? What’s the approach?  That's a great question. Not everyone knows the outcome right away. When someone has both an idea and a clear outcome, it works better, right? Because then you can help them get to that outcome. But overall, the outcomes are usually very high-level. You know: “I want to build this web application or software because I'm targeting this audience.” Okay—but what does that really mean? What problem are you solving? To be honest with you, ninety percent of people don't really know what problems they should be solving at the initial stage. So, talking about frameworks, we work with them to define which problems they should solve first. Because most startups—or even profitable companies trying to add new technology into their workflow or business—often don't know what one or two problems they should solve for the MVP before going all in. Yeah. Okay, so step one is to define the problem. What's step two?  Make sure you have the right audience for that problem. That's a big issue. A lot of times, people try to serve everyone. You don't want to go too broad, and you don't want to go too narrow. If you go too narrow, you're going to hit a ceiling before you even go to market.  So you determine the audience for the problem you're trying to solve, right?  Correct.  And then what's the next step?  Once you determine the audience and define the problem, the next best step is to create some kind of prototype and actually take it to that audience to test for product-market fit. Meaning: get feedback. Again, it doesn't have to be a fully working product. But go to that audience and get feedback like: “Yes, this solves my problem,” and “Yes, I would pay for it.” Or even better—for them to actually exchange some money to join a waitlist or gain access to an early version of the product, so they can test it and provide feedback. That's the best-case scenario. Because once you have that input, it becomes much easier to make adjustments. It doesn't matter whether those adjustments are in the design or in the actual working product—you're refining it for that niche audience. Yeah, that makes sense. So you design the prototype or minimum viable product, then you test it and get feedback. Then what do you do?  Well, I want to clarify something. Designing a prototype and having a minimum viable product can be two separate things.  Okay.  You can design a prototype. Again, it can be designed in Figma, using an AI builder, or even just as a workflow or user flow. Obviously now, things are a little different because you can build prototypes much faster. That doesn't mean they're going to be production-ready. But a minimum viable product is usually focused on solving one or two specific problems for that market. It's a problem-solving product that actually works—meaning it's much closer to being production-ready. Yeah.  So those are two separate things. There's a very big difference between them.  Yeah, because now you have vibe coding, and with tools like Lovable—or whatever platform you're using—you can create a prototype quickly. But it's not necessarily going to work, and then you still have to build the actual working product. Correct. Yes, I agree. Then you test it, expose it to the target market, and gather feedback. And then what do you do? Do you iterate? What's the next step? You iterate, yeah. So at that point, ideally, you have product-market fit, you've received great feedback from users, and—best-case scenario—they've even paid you some money. Then you either expand on what has already been built, or you go all in: invest more money into it and start building a production-ready product. And once you have that, you may realize that you also need to improve the user interface. That happens a lot—especially if you vibe-coded it. The output usually isn't the best when it comes to user interface design or user experience. So you may need to redesign the interface, properly develop it, and then take a production-ready application to market. And then it goes back into the cycle of iteration. Meaning, you keep gathering feedback. This is why I often recommend not adding too many features in the beginning. Focus on one or two core features—one or two main user flows within those features. That's it. Forget about everything else. Yeah. And then you can add features later.  You can always add features later. Most of the time, if you add too many features in the beginning, you'll probably end up cutting at least 40% of them because people just won't use them. And I'm not talking about core features like sign-up, sign-in, forgot password, onboarding, authentication—that kind of stuff. Obviously, you need those. But you still have to figure out who your audience is. Do you need SMS login? Do you need email login? Do you need both? Do you need social logins? You have to make sure you clearly understand your audience—but you don't need everything all at once. You may eventually need all of it, but not in the beginning. Yeah, that's true. So you've worked with other businesses, which means you're primarily a business-to-business agency, right?  Business-to-business, business-to-government—we've also built business-to-consumer apps as well. But usually, our client is a business-to-business.  Yeah. So here's my question: In B2B, how do you gain people's trust so they'll even engage with your product? I understand there's a funnel—but how do you get businesses into the top of that funnel? How do you create that initial trust so they engage? What does it take? Many things. Content helps, obviously. Creating content like this, creating videos—I create videos on a regular basis talking about what's out there, what's possible, what's good, what's bad. Kind of the everyday life of an agency, and the type of work we do. We also post projects on different directories and platforms. A lot of previous clients come back to us, and we get many client referrals. We rank pretty well for SEO and AEO, so a lot of people find us through ChatGPT. Especially because that's one of the services we offer. People find us when searching for things like “best app developers” or “best website designers” in our specific area. We're not targeting nationwide rankings—that's much harder and a much longer-term strategy. But in our area—Maryland, Howard County, Columbia—we rank very high.  And what does it take to rank high in AEO—in AI search?  It's the same approach we take to rank in Google. Google obviously owns Gemini, and now there's Google AI Overview. It's really a real-estate play. If you have a website that's properly structured for Google—with some adjustments for semantic search, like adding question-and-answer content to every page, especially product and service pages—you improve your chances significantly. You also need a properly configured robots.txt file with clear descriptions, so when search crawlers reach your site, they can immediately understand the structure and know where to go. When you see sources cited in AI search, that's exactly what those systems are reading from your site.  You also need the right technical setup: Your website has to be fast. You need proper H1, H2, and H3 structure across the site. So overall, it's about having a properly structured website. If you follow strong SEO fundamentals, with additional improvements specifically for AEO and GEO—because now it's not just SEO anymore, it's SEO, AEO, and GEO—you'll usually appear in ChatGPT, Google AI Overview, Gemini, Perplexity, and other AI search tools. And your Google Business Profile and Google Maps listing are properly optimized—which has changed a lot recently on Google's side as well—you'll also show up more often in local AI search results. So isn't it true that AI search looks for different kinds of signals than traditional SEO? I've heard, for example, that backlinks are less important in AI search than they used to be. They're not as important for AI search, but backlinks still carry a lot of weight. Again, you have to think about this as two separate systems, right? There's Google Search—with Google AI Overview and featured snippets—and then there's Google Maps. You don't need a website just to appear on Google Maps. You mainly need a properly optimized Google Business Profile. And you can still show up in AI search that way. Having a website does help, because it sends another signal to Google, but it's not as critical. The most important thing—and I'll answer your question for both cases—is consistency and structure. For Google Maps, if you have a properly maintained Google Business Profile with constant updates—blog posts, videos, photos, and business updates—that teaches Google AI what your business does. So you want updated product pages, images, descriptions, and location details if you're location-based.  All of that educates Google, which helps you rank higher on Google Maps. And like I said, Google Maps ranks very well in AI search. Now, if you also have a website, that's even better. And on your website, it helps to embed your Google Map as well, because that reinforces another signal from Google Maps. For example, some of our clients have multiple locations, so we include Google Maps with all their locations on the site—and that helps. Then you also create location pages, just like you create product pages or service pages. Google—and AI systems in general—don't really rank entire websites. They rank individual pages. That's why top-of-funnel content is usually blog posts or educational content answering someone's problem. Then that written or video content leads users to a service page or product page. That's basically how it works. Does that make sense? Yeah, that's very interesting. So if I want to increase my AI ranking… one of my clients told me that if your clients post about you on Reddit, that can be really powerful and help drive AI search visibility. Is that true? Reddit and Quora are very powerful. Very powerful. They rank very high. Listen, I'll give you a simple example that anybody can use. If you go to Quora or Reddit and look at the questions people are asking—for example, let's say you search for “app development”—you can filter by questions and literally see what people are asking. If you answer those questions in a natural way, related to your service or product, and include a backlink—not in a salesy way, but naturally—that's a very strong backlink. And speaking of backlinks: they're still relevant. Maybe they don't carry as much weight as they used to, but they're still very valuable.  Because when Google or AI systems evaluate content—and when you search in ChatGPT, Claude, or Gemini and see sources—those sources are essentially citations and backlinks. So if your website has strong citations and is properly structured, it absolutely helps you get discovered. You just need to make sure everything is set up correctly so Google—or any other search system—understands what your content means. But yes, to answer your question directly: Reddit and Quora are excellent for visibility because they're high-authority websites with massive traffic and very strong domain ratings. Yeah. That’s great. So Google Maps, Reddit, Quora, they are big drivers. That’s great.  Huge drivers. I mean, listen, there are many others—but social media has become huge over the past two years. Before, if you made a Reel on Instagram, you wouldn't be able to find it through Google search. But in the past couple of years, they opened that up. Why do you think they did that? Because they understand the value of content. Just like YouTube—where you can find videos through specific keywords—they want Instagram videos to be discoverable through Google Search and AI search. And then those searches lead people back to their platform. If someone who isn't already an Instagram user discovers content they like—a creator they like—they may sign up for Instagram because of it. So yeah, all of this ties back to backlinks and discoverability. It's really about how you use those backlinks. I mean, YouTube has been a huge driver for people looking for answers or trying to learn almost anything. So yeah, that's kind of how it works. It's one big spiderweb. Yes. It’s interesting. So basically, the more content I have and the more content other people post about me in credible sites, whether it’s Reddit, Quora, YouTube, social media, and they all point to my website or web pages, then the more it’s going to be discoverable by AI. That’s kinda makes sense.  You're definitely going to become more discoverable. But again, if it's just “Steve Preda,” that alone may not be valuable unless someone is specifically searching for your name. Now, if people are responding to or discussing how to apply a specific framework—and someone is searching for that framework that relates to your content—then it becomes relevant. Does that make sense?  Yeah. Yeah, understand. Yeah. Absolutely. Let me ask you this. If you could have a magic wand and fix one thing inside your company in the next 12 months, what would that be?  That’s an interesting question. I don’t know. I think I'd be very interested in applying more AI agents so they can help drive the business and support more growth. Overall, I just want healthy growth—making sure we're happy with the work we're doing, and that our clients are happy with the work we deliver. Because that leads to better outcomes, longer-term relationships, and healthier growth for the company. I mean, my ultimate goal at some point is probably to grow the company and eventually sell it. If we're happy with what we're doing, and our clients are happy with the work we're delivering, I think that growth will happen organically. Yeah. And what do you need to make the company sellable in your perspective?  Having strong, scalable systems—and AI is going to help with a lot of that.  So do you believe that a company with only AI employees—at the extreme—could still become a very valuable company? No, I'm not saying we should rely only on AI, and I'm definitely not planning to let go of any employees. What I'm saying is that AI can help with certain smaller tasks that sometimes get missed or forgotten. That's a perfect fit for AI. For example, even during conversations—if a project manager is handling several clients at once—we usually need updates on what was discussed. Yes, AI can record the conversation, but more importantly: what are the actionable next steps? And from those action items, what has already been completed, and what still needs to be done? Those are the kinds of things AI agents can help with—tasks that don't necessarily require a human. That way, time isn't wasted and can instead be used more effectively to make sure things are getting done and that we're reaching the outcome you mentioned earlier. What is your opinion about controlling AI agents? What is the level of risk? Not just about someone maybe doing a prompt injection and kind of hijacking your agents, but losing control of the agents in terms of complexity. So do you see a risk there that someone could kind of unleash these agents and somehow not be able to control them, or the quality of their work? Could they not control that? Or something changes and the agents get impacted—maybe a software update or something like that? Is this a thing, or is that not a concern? I think there should definitely always be guardrails. For example, right now we're building a platform with AI to gather RFPs, review them, score them, and actually create outputs—like the structure of the RFP. But before they get submitted, an actual person reviews them. I think there should always be final approval by a human—unless it becomes such a perfect system. I mean, it's software, right? At a certain point, can something go wrong? Yes. Especially with updates—unless you own the full process from beginning to end. Yeah, I think there's always a risk, but there's always a risk with software.  There should definitely be some guardrails, no doubt about it. I don't think it should be the last step before a human approves it and actually—for this RFP example—submits the response to whatever platform. I think a human should always review and approve it to make sure everything is working properly. But I think you can save a lot of time. For example, instead of us doing two or three RFPs a month, we can do ten or fifteen. I mean, the quality isn't really changing. It's structure. It's answering what they're asking for. So if it fits the criteria we're looking for, we still spend time reviewing it. I mean, we got an RFP the other day that was 150 pages. It would probably take two days just to read it. And at a certain point, you're like, “You know what? This isn't a good fit.” So it saves time. It just creates more efficiency. But there should definitely be guardrails and structure for sure, and a human should be involved in the loop. That I agree with you on. Okay. It's a big topic. One of the thoughts is that at some point AI is talking to AI. Like in hiring—you see these big recruiting companies using AI to filter resumes, and then applicants use AI to write resumes that fit what the filters are looking for. And at some point, the authenticity or credibility of those resumes begins to fade because it's all prearranged. So then the whole purpose of filtering employees starts to diminish. Do you think this kind of thing might happen with RFPs too? Maybe. Very possible. I wouldn't be surprised if it's not happening already. Yeah, I mean, it's definitely very possible. There are already several platforms that find RFPs. They work a little differently. We're building specifically for our own purpose. I do want to document the process to kind of show, “Hey, here's what can be done.” But yeah, it's very possible, for sure. Listen, if you're relying on a regular process to get a job, then you're probably not going to get the job. There are a lot more people looking for work right now. I don't know if you heard about Microsoft—and I think Tesla too—but companies are letting people go left and right. Microsoft is offering long-term employees buyouts. And by long-term employees, I mean people who are probably older and maybe not as knowledgeable or experienced with AI.  It's like, “Hey, let us buy you out so you can retire a little earlier.” So this is happening. If you're going through the same regular hiring process as everyone else, you're competing against 500 or 1,000 other people for the same job. Obviously, it's an employer's market right now, not an employee's market. If you're trying to get a job, it shouldn't just be through the regular process. It should be through people you know. Networking is going to have even more value. Personal connections matter, and people knowing, “Hey, this person actually spoke to me the right way.” You should also know how to use AI, because that's going to give you an edge in getting a job. But actually speaking to someone should happen through networking and connections. Yeah, that's my feeling too—that human interaction is actually going to increase dramatically in value. Because authenticity… that's really the only way to verify authenticity: being face-to-face with someone, a real physical person. That's fascinating. Yeah. But I'll tell you—like I said, I post videos on a regular basis. My mom asked me the other day, “Max, are you using AI, or is it really you?” I said, “No, it's really me. It's not AI.” So it's funny because AI is getting so good that you're not always sure what's real anymore. And even with RFPs—it's not just about submitting proposals or resumes. Personal and human connection is going to become more valuable than ever. If I personally knew every buyer putting out an RFP, I'd rather talk to them directly, one hundred percent. Because it becomes a completely different process.  Yeah, that's spot on. Love it. So, great information. I love the framework: define the problem, determine the audience, create a prototype, build the MVP, test it, and then iterate. That's how you build a digital product—whether it's a website or an app. So if you're out there looking for a solution, Max Kryzhanovskiy and MOS Creative may have the solution for you. So if people would like to connect with Max Kryzhanovskiy and MOS Creative, where can they reach you? People can reach us through our website: www.moscreative.com. They can also find me on LinkedIn under Max Kryzhanovskiy or MOS Creative. They can fill out a form on our website or email us at info@moscreative.com. Fantastic. So if you want an AI-driven platform, definitely reach out to Max. So Max, thank you for coming and sharing your ideas. And I love that you have such a strong vision for AI and that you're actively experimenting within your company, which means your clients will benefit from that as well. And if you enjoyed this conversation, then stay tuned, because every week a successful entrepreneur comes on the show and shares their ideas and frameworks. So thanks for coming, Max—and thank you for listening. Thank you. Important Links: Max's LinkedIn Max's website Max's email: info@moscreative.com

AI for Designers is officially open for enrollment for just a couple of days (or until it's sold out)- In this special episode of Future of UX, I'm answering all the biggest questions about my live bootcamp AI for Designers.We talk about:how the bootcamp workswhat's includedhow much time you actually needAI workflows for designersvibe coding, AI UX, research, prototyping & content generationthe live workshops and communitywhy AI skills are becoming essential for designersI also share more about this cohort's exclusive extras, including:an exclusive Figma collaboration workshopan AI Leadership workshop with Penny Blackmoreupdated workflows, assignments, and live sessions

What if the most important skill for building AI products has nothing to do with evals, technical background, or knowing how to write a prompt? What if it is the ability to design systems that can handle what you never planned for?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Apurva Garware, who has built and scaled products across Amazon, Microsoft, and Upwork, to make the case that systems thinking is the defining skill of the next era of product management. Apurva explains why non-determinism forces PMs to stop thinking in features and start designing the guardrails, agent contracts, and escalation points that govern how a system behaves at runtime, when no one is watching. They explore a three-phase framework for governing AI systems across design, deployment, and production; heuristics for deciding what to hand to agents versus escalate to humans; and a sharp insight about the two products every AI-native company is actually building: the customer-facing product, and the internal operational system that drives margin and velocity. Marc and Ben also share their own experience calibrating an agentic workflow at Supra, grounding the conversation in practice.If you are a PM trying to find your footing in the AI era without a deeply technical background, a founder wrestling with when to reach for AI versus simpler deterministic automation, or a product leader who wants to build more discipline into how your team ships AI products, 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

Ryan Burke, VP of Worldwide Sales at Crogl, joins Sam Jacobs, AJ Bruno, and Asad Zaman on the new economics of enterprise cyber risk. Topics include Anthropic's Mythos model, AI for the security operations center, why vibe-coded apps are far more likely to have security issues, why Claude Design tanked Figma's stock, and what the Elon Musk versus OpenAI lawsuit signals for AI governance. Key takeaways: AI has crashed the cost of running sophisticated attacks, putting nation-state-grade tooling in the hands of low-skill operators. As Ryan Burke, VP of Worldwide Sales at Crogl, put it on Anthropic's Mythos model: "Mythos has lowered the cost to like the dollar menu equivalent of...running an attack...so more people can do it." Enterprises are staring down a multi-year patching backlog that runs from now until the end of time. Non-technical teams in finance, ops, and HR are shipping internal tools using Replit and Claude, and almost none of them are securing what they build. Ryan Burke flagged the research: "vibe-coded software is almost 3 times as likely to have security issues." When the employee who built the agent quits, the agent stays behind with no owner, no documentation, and quiet access to systems it never should have had in the first place. For founders eyeing an exit, security has joined revenue, IP, and hitting your numbers as a non-negotiable diligence pillar. As Ryan Burke explained: "lack of security can kill an acquisition...a fourth pillar now is you're secure." Acquirers like JPMorgan Chase will not buy a fintech startup that turns into a vector for attackers to walk straight into their environment. The market case for NRR-fortress legacy SaaS may be weaker than the last decade made it look. As Asad Zaman, CEO of Sales Talent Agency, argued: "there was a generation of software companies that had signs that they had really good customer relationships...but their customers felt more like prisoners." If AI makes switching cheap and a new generation of software actually delights users, the moats around system-of-record incumbents start to compress fast. Connect with the hosts and guest:  Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/  Guest: Ryan Burke, VP Worldwide Sales at Crogl - https://www.linkedin.com/in/ryan-burke-bos/ 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 Ryan Burke 03:14 Anthropic Mythos and Cyber Risk 04:20 How Attackers Use AI at Scale  07:00 Dollar Menu Attacks Explained  10:41 AI for the Security Ops Center  14:53 Why Claude Tanks Figma's Stock  18:30 Sam's Advice on Falling Stocks  20:50 Are Legacy SaaS Companies Back?  24:04 The Vibe-Coding Risk Surface  27:56 Quiz Pro: Cybersecurity Edition  33:46 Replit Apps Inside Enterprises  40:18 Security as the M&A Fourth Pillar  44:17 Personal Data and Digital Legacy  47:24 Bulls vs Bears: Elon vs OpenAI  52:03 Will ServiceNow Hit $32B?

In this episode of the Grow A Small Business Podcast, host Troy Trewin interviews Gulliver Moore shares how he scaled Sunday Treat alongside his co-founder Oliver Clubb from a freelance director/DP partnership into a £2M creative agency serving global brands like Google, Disney, and Revolut. In this episode, Gulliver reveals how consistent 20% year-over-year growth, smart hiring, strong company culture, and high-performing video marketing helped the agency expand to a 14-person team with clients across the US and Europe. He also explains how Sunday Treat is adapting to AI, building viral content strategies, and maintaining creativity while scaling fast. Gulliver shares honest lessons about leadership, delegation, difficult management decisions, and why founders should never delegate hiring. The conversation is packed with insights on business growth, branding, team building, and creating a sustainable agency in today's competitive digital landscape.  Why would you wait any longer to start living the lifestyle you signed up for? Balance your health, wealth, relationships and business growth. And focus your time and energy and make the most of this year. Let's get into it by clicking here. Troy delves into our guest's startup journey, their perception of success, industry reconsideration, and the pivotal stress point during business expansion. They discuss the joys of small business growth, vital entrepreneurial habits, and strategies for team building, encompassing wins, blunders, and invaluable advice. And a snapshot of the final five Grow A Small Business Questions:  What do you think is the hardest thing in growing a small business? Gulliver Moore shared that the hardest part of growing a business is consistently delivering on big promises while maintaining quality and trust with clients. What's your favorite business book that has helped you the most? Gulliver Moore said his favorite business book is Radical Candor because it deeply influenced how he manages people, gives feedback, and builds an honest company culture. He also highly recommended The Making of a Manager for its practical advice on leadership and team management. Are there any great podcasts or online learning resources you'd recommend to help grow a small business? Gulliver Moore recommended podcasts like Hard Fork, The Vergecast, and Today in Focus to stay updated on technology, AI, and current events. He also emphasized learning through experimentation, especially with AI tools, social media content creation, and hands-on business experience rather than relying heavily on formal coaching or consultants. During the conversation, host Troy Trewin additionally recommended Marketing School, Uncensored CMO, and Everyday AI for marketing and business growth insights. What tool or resource would you recommend to grow a small business? Gulliver Moore recommended using Claude AI for brainstorming, strategy, copywriting, and improving workflows with AI. He also highlighted Monday.com as a powerful CRM and project management system for organizing teams and client work, while Figma was his preferred platform for creating visually engaging presentations and creative assets. He emphasized that combining strong systems, consistency, and AI tools can significantly improve productivity and business growth. What advice would you give yourself on day one of starting out in business? Gulliver Moore said he would tell himself to trust the process, stay patient, and focus on consistently hiring great people. He emphasized that long-term success comes from building a strong team culture, trusting your instincts during hiring, and sticking with the journey even when growth feels slow or uncertain. Book a 20-minute Growth Chat with Troy Trewin to see if you qualify for our upcoming course. Don't miss out on this opportunity to take your small business to new heights! Enjoyed the podcast? Please leave a review on iTunes or your preferred platform. Your feedback helps more small business owners discover our podcast and embark on their business growth journey.     Quotable quotes from our special Grow A Small Business podcast guest: Hiring the right people is the most important investment you can make in your business — Gulliver Moore You don't need to control everything — great teams do amazing work when you trust them — Gulliver Moore Consistency in your process will eventually create the growth you're looking for — Gulliver Moore       

Change management decides whether your MES or digital transformation project lasts, or quietly gets shut off six months after go live.Vlad Romanov and Dave Griffith sit down with Logan Terry, who leads digital transformation at LSI, to dig into change management as the deciding factor in any automation or MES rollout. Logan defines change management as a methodical approach to moving an individual, team, or organization from a current state to a desired future state. The closer a system sits to where decisions are actually made, the more change management it requires, which is why MES is the single hardest place to land a project successfully.Much of the episode digs into why change management is rarely scoped properly. In competitive RFPs, the integrator who includes a robust change management line item often loses to the lowest bid, and end users frequently do not know how to evaluate that line item even when it is offered. Logan starts every client engagement with a direct question: what does your continuous improvement practice look like internally? If the client cannot sustain the change after handover, the project is on borrowed time no matter how clean the FAT and SAT looked.Logan walks through one of the most useful failure stories on the show this year. His team delivered a technically perfect OEE dashboard for a production line. Six to nine months later, every terminal was shut off. The postmortem surfaced two missed details. Maintenance was never folded into the design, and a single failed photo eye broke throughput calculations with no manual reconciliation path, which destroyed operator trust in the data. The second miss was behavioral. Showing a 30 percent OEE against a 90 percent ideal demotivates the floor, while reframing the same number as 80 percent of a realistic 36 percent target turned out to be a cleaner motivator.Looking forward, Logan sees vendors moving away from monolithic 14 function MES suites toward modular, use case specific deployments, which compresses change management scope from twenty five workflows to five or six. On AI, he argues that managing generative agents in production is closer to managing a team of people than managing software, with continuous validation replacing one time qualification. He cites the line that AI does not make bad data worse, it makes it more convincing. LSI now uses AI assisted coding agents and React based prototypes to shrink design cycles from three or four weeks of Figma work down to three or four days.About Logan TerryLogan Terry leads digital transformation at LSI, a multinational systems integrator with roughly 400 resources across 13 North American locations and offices in Asia Pacific. A mechanical engineer by training, Logan spent a decade in PLC, HMI, and SCADA development before moving into digital transformation consulting and joining LSI in late 2024. His work spans advanced SCADA, MES, analytics, and BI integrations.LSI: https://www.logicalsysinc.com/Timestamps0:00 Introduction2:15 Logan's background and the LSI digital transformation practice7:25 Defining change management9:00 Why MES requires the most change management13:00 How young engineers stumble into change management24:30 Starting with decisions and workflows before technology35:00 Internal CI capability as a project gating factor43:30 OEE dashboard turned off six months after go live46:30 Behavioral psychology of how operators read numbers54:50 Modular MES replacing monolithic platforms58:00 Generative AI and continuous validation1:11:00 AI assisted prototyping shrinking design cyclesAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladimirromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Digital Transformation in Manufacturing: https://www.joltek.com/blog/digital-transformation-in-manufacturingManufacturing Execution Systems and Business Strategy: https://www.joltek.com/blog/manufacturing-execution-systems-business-strategyDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

Elad Gil (@eladgil) is CEO of Gil & Co, a multi-stage investment firm, holding company, and operating company working on the world's most advanced technologies. Elad is a serial entrepreneur, operating executive, and investor or advisor to private companies, including AirBnB, Anduril, Coinbase, Figma, Instacart, OpenAI, SpaceX, and Stripe. He was previously VP of Corporate Strategy at Twitter and started mobile at Google. He was the founder and CEO of Mixerlabs and Color. Elad is the author of the bestseller High Growth Handbook: Scaling Startups from 10 to 10,000 People.This episode is brought to you by:Matic the intelligent robot vacuum and mop that navigates obstacles and needs no babysitting: MaticRobots.com/TimAG1 all-in-one nutritional supplement: DrinkAG1.com/TimEight Sleep Pod Cover 5 sleeping solution for dynamic cooling and heating: EightSleep.com/Tim Helix Sleep premium mattresses: HelixSleep.com/TimTimestamps[00:00:00] Start.[00:02:21] What's the “AI personal IPO” that just quietly happened across Silicon Valley?[00:05:28] Tens to hundreds of millions per researcher: What top AI pay packages actually look like.[00:06:44] The compute ceiling: Why Korean memory fabs are the unlikely bottleneck throttling every AI lab on earth.[00:11:11] From zero to $30B run rate: The fastest revenue ramps in the history of capitalism.[00:17:24] The dot-com survival rate was one in 100. Buckle up, AI founders.[00:20:35] Your value-maximizing window: Why the next 12–18 months may be as good as it gets.[00:21:32] Durable advantage — and why the AI market is an oligopoly (for now).[00:24:12] Exit options for AI founders: labs, hyperscalers, vertical players, and the underrated merger of equals.[00:28:11] Math, biology, and intuitive leaps: Elad's pre-investing background.[00:29:42] Elad's revisionist genesis story.[00:30:50] Go where the cluster is: 91% of global AI private market cap lives in a 10×10 mile square.[00:33:20] The accidental investor: Patrick Collison walks, Airbnb intros, and deals that just happened.[00:34:37] Want money? Ask for advice. Want advice? Ask for money.[00:35:00] The High Growth Handbook: Tactical guide, not bedtime reading.[00:35:41] Market first, team second — with a Perplexity-and-Anduril asterisk.[00:37:43] Smoke in the distance: AlexNet and the transformative GPT-3 moment.[00:45:15] AI cold-reading: Feeding photos to the model and getting eerily accurate personality reads.[00:48:56] Has Elad ever done a retrospective on his own investing?[00:52:13] Power laws are terrifying: 10 companies, 80% of returns, two decades.[00:55:53] Avoiding science projects, and how SPACs accidentally saved hard tech investing.[00:59:20] The one-belief framework: Coinbase = crypto index. Stripe = e-commerce index. That's the whole memo.[01:00:54] Due diligence theater vs. the one question that actually matters.[01:02:13] The four-year vest is a relic: How venture capital ate growth investing.[01:07:16] Boards as in-laws: You can't fire them, so choose wisely.[01:09:47] “Valuation is temporary. Control is forever.” — Naval Ravikant, as quoted by Elad, as relayed to you.[01:11:30] How great companies actually grew: toolbars, name-targeted ads, and billions in distribution spend.[01:15:36] Selling software vs. selling labor hours: The real shift generative AI made.[01:18:40] Spotting a great market: regulatory shifts, technology shifts, and Hashi getting bought by IBM.[01:21:28] Fake TAM, real TAM, and the Coke CEO who realized he wasn't in the soda business.[01:22:47] Right now, consensus is just correct. Save the contrarianism for later.[01:25:15] Market entry vs. market disruption: SpaceX launched rockets, then disrupted the internet.[01:26:16] How Elad learns: X, papers, 20-minute calls with the right people — and four AI models running in parallel.[01:27:15] Deep dive: ADHD, autism, and why diagnostic rates soared without more people actually having it.[01:33:40] Longevity for realists: sleep, creatine, and maybe rapamycin when the real drugs arrive.[01:40:30] Ibogaine, anesthesia, and the next frontier of bioelectric medicine.[01:45:15] Elad's first-ever 10-year plan — and why making one changes everything.[01:46:53] Parting thoughts.*For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The NIA boys discuss Adobe vs. Figma vs. Canva vs. Claude DesignTimestamps(00:00:00) - Intro(00:03:28) - Adobe vs Figma breakdown(00:14:03) - Canva(00:17:12) - Claude Design(00:23:26) - Claude Design will eat Figma(00:26:33) - Claude Design Demo(00:36:54) - Positioning of Canva vs Figma vs Adobe(00:54:27) - Jack's Design Stack What Is Not Investment Advice?Every week, Jack Butcher, Bilal Zaidi & Trung Phan discuss what they're finding on the edges of the internet + the latest in business, technology and memes.Subscribe + listen on your fav podcast app:Apple: https://pod.link/notadvicepod.appleSpotify: https://pod.link/notadvicepod.spotifyOthers: https://pod.link/notadvicepodListen into our group chat on Telegram:https://t.me/notinvestmentadviceLet us know what you think on Twitter:http://twitter.com/bzaidihttp://twitter.com/trungtphanhttp://twitter.com/jackbutcherhttp://twitter.com/niapodcast Hosted on Acast. See acast.com/privacy for more information.

Presales teams are being asked more and more to do post-sales work. This isn't a fad or something we're doing in the short term. This is a significant change for the broader profession of presales and solutions consulting. What was once occasional support for implementations or customer renewals is now becoming formal job responsibilities for solutions consultants across the industry.   In this episode, Jack Cochran sits down with Shamil Turner, Global Technical Solutions Leader at Figma and Presales Collective advisory board member, to explore why this shift is happening and what it means for the future of presales.   Shamil brings a unique perspective to this conversation: he was the first SE hired at Figma, built their entire solutions engineering organization from the ground up, and has now transitioned into a post-sales technical leadership role. Together they discuss the strengths that SEs bring to the table that has a profound impact on a company-customer relationships AFTER the sale has happened, and how this plays out in a new role that has been emerging over the past year, the Forward Deployed Engineer. Whether you're a leader making organizational decisions or an IC just getting started, this conversation will help you understand how the world of solutions is evolving. Thank you to Saleo for sponsoring this episode! Follow Us Connect with Jack Cochran: https://www.linkedin.com/in/jackcochran/  Connect with Shamil Turner: https://www.linkedin.com/in/shamil-turner/  Links and Resources Mentioned Join Presales Collective Slack: https://www.presalescollective.com/slack  Sol/Con 2026 (Chicago, August 2026): https://www.presalescollective.com/solcon-2026  Presales Collective Podcast: https://www.presalescollective.com/podcast Saleo: https://saleo.io  Key Topics Covered The Growing Trend of Presales Teams Doing Post-Sales Work Historical Context: How SEs Have Always Supported Post-Sales Forward Deployed Engineer Roles in Consumption-Based Models Why Presales Skills Are Valuable for Customer Onboarding Shamil's Journey from First SE at Figma to Global Technical Solutions Leader The Convergence of Pre and Post-Sales Functions Career Implications for Presales Professionals at All Levels What This Means for the Future of Solutions Organizations  

BONUS: AI Won't Just Change How You Work — It Will Reshape Your Organization The Global Agile Summit is around the corner, and the AI in Organizations track is one you don't want to miss. In this episode, track co-hosts Michael Dougherty and Michał Parkoła walk us through what they've built — from the thinking behind the track name to the sessions that stood out, and why this isn't just another AI conference lineup. Why "AI in Organizations" — Not Just "AI" "AI will not only be useful to existing organizations, but it will reshape organizations in a very significant way, the same way cars reshaped cities."   Michael and Michał drew a deliberate line with the track name. Michael points out that AI has been around for decades — it didn't start with ChatGPT. The real shift now is AI agents scaling to enterprise level, replacing automation that used to require specialized tools. Claude Enterprise holds about 29% of the enterprise AI market, Gemini around 15%. But Michał pushes the framing further: the first-order effect is applying AI to existing work. The second-order effect — the one he's most interested in — is how AI will reshape organizations themselves. New species of companies will emerge, smaller teams will achieve what used to require hundreds of people, and some existing organizations won't survive the transition. That's the conversation this track is designed to start. Filtering the Signal From the Slop "There was a bit of AI slop in the submissions. There was a lot of talk that, unfortunately, was meta-talk — there was no real value that I could glean."   When session submissions came in, Michael was disappointed by how many were surface-level — big promises with no practical takeaway. The ones that stood out were practitioners showing what they actually do. Dave Westgarth, for example, demonstrated how he uses AI with Lovable and Claude embedded in Miro whiteboards to enhance real team interactions. On Michał's side, the standout was Max Pirata, who challenged the "vibe coding is slop" narrative. His argument: the quality of large-scale software has never depended on the infallibility of individual engineers — it depends on disciplined engineering processes. The same applies to agentic engineering. Your first attempt at vibe coding will be rough, but there are ways to apply engineering discipline to AI-assisted development. That's what Max will be talking about at the summit. Prototyping at the Speed of Thought — And the Human Bottleneck "Now I've got 20 prototypes that I can choose from. Which ones are the best? Which ones do I need to clear out? Product managers now have a different game they play."   Two sessions capture opposite sides of the AI-in-organizations tension. Dave Westgarth's "Vibe UX: Prototyping at the Speed of Thought" shows how vibe coding lets you build full working systems instead of Figma mockups — so fast that the bottleneck shifts from creation to selection. Product managers and product owners now face a new challenge: clearing the closet of AI-generated options rather than validating a single bet. On the other side, Shawn Wallack's session — "Even With AI, Your System Will Never Be Better Than Its People" — brings the counterpoint. Michael explains the systems-thinking angle: AI does what you tell it, fast and accurately, but that speed reveals human bottlenecks everywhere else. He shares the cautionary example of AI declining twice the insurance claims humans did, with the human-in-the-loop rubber-stamping instead of actually checking — leading to a class action lawsuit. The lesson: AI doesn't remove the need for human judgment, it makes it more critical. Gojko Adzic on Spec-Driven Development and Building AI Products "True to his roots, he is exploring spec-driven development now, which is one of the popular threads in agentic engineering."   Gojko Adzic — the author of Specification by Example and Impact Mapping — brings heavyweight credibility to the track. Michał reveals that while Gojko is exploring spec-driven development in the context of agentic engineering, the interview focused more on his hands-on experience building his own AI products. For attendees, this means real practitioner insights from someone who literally wrote the book on how specifications drive software quality — now applying those principles in an AI-first world. From Beginner to Builder — Who This Track Is For "My favorite case would be people who will quit their jobs and start new companies that will be able to achieve wonderful things with much smaller teams than we would otherwise imagine possible."   The track is designed to meet people wherever they are. Pierre Beaning covers the basics of using Claude for beginners. Jason Little — who Michael describes as a "techno nerd" and "grand poobah" — shows how to build and scale multi-agent systems for business. The spectrum runs from "I've only used AI to plan a vacation" to "I'm orchestrating agent teams." But Michał's vision for the ideal attendee is bolder: someone who walks away ready to start a company. Michael backs this up with the story of an AI unicorn — $1.8 billion valuation, one guy and his brother, in the pharmaceutical industry, just a few months old. Hype? Maybe. But Michał's pragmatic take lands it: "If you make a few million, even if it dies later, that's not such a bad thing." The goal of the track is to blow away the fog — throw flares into key spots so people can sketch a map of what's possible and decide which areas deserve a follow-up. About Michael Dougherty Michael Dougherty is the Co-author of Shift: From Product to People, leadership coach with 30+ years helping organizations adopt people-centered, agile ways of working. Co-owner of the Global Agile Summit.   You can link with Michael Dougherty on LinkedIn and find out more at shiftingpeople.com. About Michał Parkoła Michał Parkoła is an Agile practitioner based in Warsaw, Poland. Previously hosted the Value-Centric Product Development track at Agile Online Summit 2024. He is building Tapestry, an AI planning assistant.   You can link with Michał Parkoła on LinkedIn and check out Tapestry at growwithtapestry.com.

In this episode Scott Becker shares a quick look at steep declines for Figma, Lucid Motors, and Klarna, highlighting how once high-flying companies are struggling with sharp losses and market pressure.