Podcasts about Notion

Waziri Garuba, creator of G.R.I.O.T and CEO of HarlemLabs, talks about how he was inspired by the limitations of Siri and where the name for his custom AI persona, G.R.I.O.T, comes from. He  integrates several platforms including n8n.io, 11 Labs, Vercel, and it can be integrated with an LLM of choice, including Claude. The system operates using an agentic workflow where a central brain assesses inputs via webhooks. Incoming queries are classified into four categories: direct, tools, complex, or schedule-based to determine the appropriate routing. He explains how his agent can talk to the platforms. The First Automation Waziri describes how he built his first automation which was an auto email response maker designed to monitor Gmail every minute. The agent reviews unread messages and drafts responses in his specific writing style while logging actions in a spreadsheet. His current evolved system uses a contact map to identify known contacts and manages inbox organization through classification and labeling. Tracking Health and Fitness Waziri talks about how he uses G.R.I.O.T in his personal life. He calls it G.R.I.O.T in the Garage. Waziri utilizes a Notion database to track health reports and personal fitness goals. The G.R.I.O.T agent can access this data to design customized workouts and automatically add them to his calendar. He demonstrates how the agent can interact with his calendar to move appointments or set reminders based on terminal tasks. The Technical Landscape Waziri explains how G.R.I.O.T can integrate with many tools and finds the best tool for the job. The technical landscape consists of specialized sub-agents for platforms like Airtable, Google Drive, Monday, and Notion. These agents act as "appendages" that handle specific tasks such as searching files or managing relational databases. Waziri utilizes Tavily, an AI-driven search tool used by developers. It operates like Perplexity to help the agents find real-time information. Mobile Accessibility Waziri  interacts with G.R.I.O.T through Telegram, allowing him to send voice memos while on the go at the gym or traveling. This mobile interface enables him to delegate research, schedule follow-ups, or draft emails using simple spoken commands. He prefers Telegram over other messaging apps because it offers easier integration with n8n for building custom workflows. He explains how it helps him manage his emails, both incoming and backlog, and newsletters. The Finance Tools A dedicated suite of finance tools helps Waziri manage debt, income tracking, and expense reporting through QuickBooks and Google Sheets. The system implements a behavioral mechanism to automate saving ten percent of incoming funds into a separate account. One specific agent provides regular reports on interest rates for student loans and credit cards to help prioritize debt repayment strategies. Professional Services Waziri serves CEOs and high-level executives by helping them map out their operational needs before implementing AI tech stacks. He offers an eight-week course called the Operators Map at theoperatorsmap.com to teach governance and automation design. His business engagements focus on helping operators transition from manual processes to efficient, automated systems through HarlemLabs.com. This episode on Umbrex: https://umbrex.com/unleashed/episode-657-waziri-garuba-ceo-of-harlem-labs-introducing-g-r-i-o-t/ Video permalink:  Timestamps: 00:02: Introduction to G.R.I.O.T Persona  06:12: Automating Email Management  12:15: Personal Health and Task Integration  17:24: Agent Architecture and Tooling  37:53: Mobile Accessibility via Telegram  38:53: Financial Automation Systems  43:36: Professional Services and Education Links: HarlemLabs website: https://www.harlemlabs.com/ Operators Map website: theoperatorsmap.com   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

Our 255th episode with a summary and discussion of last week's big AI news!Recorded on 08/26/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:SpaceXAI released Grok 4.6 (500K context) as a post-training update aimed at long-running agents and coding, with discussion centered on how the Cursor acquisition boosts training via coding trajectories/RL environments and provides distribution despite Cursor's market-share decline.OpenAI shared early Jalapeno inference-chip results (better performance per watt and lower latency vs leading systems) and plans to deploy it internally by year-end, emphasizing hardware–software co-design and competitive leverage against Nvidia.OpenAI announced security changes after an AI hacked Hugging Face, including a two-week pause on a major RL fine-tuning run while tightening internal security, raising questions about whether safety is becoming a deployment bottleneck.Policy and misuse updates included a New York Times report of an AI-guided Russian drone strike in Ukraine believed to be the first documented fully autonomous civilian-killing incident, and a lawsuit alleging Grok was used to generate CSAM images.A thank you to our current sponsors:Box - visit Box.com/AI to learn moreNotion - go notion.com/lwai to try Notion's Developer Platform today.ODSC AI - go to odsc.ai/east and use promo code LWAI for an additional 15% off your pass to ODSC AI East 2026.Factor - head to factormeals.com/lwai50off and use code lwai50off to get 50 percent off and free breakfast for a yearTimestamps (these may be slightly off due to sponsor inserts):(00:00:10) Intro / Banter(00:01:47) News Preview(00:02:52) Response to listener commentsTools & Apps(00:03:32) Google announces Gemini 3.7 Flash just three weeks after previous release - Ars Technica(00:13:11) SpaceXAI Releases Grok 4.6: A 500K-Context Frontier Model Tuned for Long-Running Agents, Coding, and Knowledge Work - MarkTechPost(00:22:32) Claude will apply invisible watermarks to AI text and images | The Verge + Anthropic explains how Claude's invisible text watermarks will work(00:28:50) Bringing the cybersecurity capabilities of Claude Mythos 5 to more defenders | Claude by Anthropic(00:32:20) OpenAI to Roll Out Enhanced Safety Features for Paid AI Tool Users - Bloomberg(00:33:41) ChatGPT's Stricter Teen Mode Starts Rolling Out Today(00:34:43) Meta AI Now Has A Dedicated Desktop App For MacApplications & Business(00:37:33) Jalapeño's first results show industry-leading speed and efficiency in AI inference | OpenAI(00:45:35) OpenAI loses a top data center exec as stream of high-profile departures continues | TechCrunch + OpenAI talent exodus raises 'huge red flag' ahead of IPO(00:50:29) Anthropic Taps Google Chip Veteran as Part of Push Into Hardware(00:52:30) Anthropic's annualized revenue surges to $65B | TechCrunch(00:59:30) Thomson Reuters launches in-house AI model to cut Anthropic costsProjects & Open Source(01:04:26) Qwen 3.8: How a 27B Open Model Rivals GPT-5.6 and Claude OpusPolicy & Safety(01:08:27) A Drone Killed Three Ukrainians. It Was Guided Entirely by A.I. - The New York Times(01:17:59) OpenAI lays out new security changes after its AI hacked Hugging Face | The Verge + OpenAI institutes new safeguards after Hugging Face breach + https://openai.com/index/pacing-model-development-cyber-capabilities/(01:23:31) Another Woman Joins Lawsuit Accusing Grok Of Generating CSAMResearch & Advancements(01:24:56) Small-Scale Experiments: Are We There Yet?(01:29:21) Stealing Reasoning Traces from Proprietary LLM APIs(01:34:30) Massive Activations in Hybrid Linear Attention Large Language Models: Pre-Attention Spikes and Inter-Spike Plateaus Synthetic Media & Art(01:38:59) AI Slop Is Everywhere. Spotify, LinkedIn and Others Have Had Enough. - The New York TimesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Is your accounting firm stuck between AI enthusiasts who move too fast and skeptical team members who refuse to adopt new technology? Bridging the gap between enthusiastic leadership and hesitant team members is one of the biggest challenges facing modern firms today. In this episode of Growing Your Firm, host David Cristello sits down with Jan Haugo, founder and AI researcher at smartaccountant.ai and co-author of Intuit's official AI certification curriculum. Jan researches and validates 50+ emerging AI tools annually for controllership, Record-to-Report (R2R), AP/AR, and advisory workflows. Jan breaks down how to bring your entire firm onboard with AI safely, navigate critical security and redaction requirements, and use AI to transform sales, onboarding, and advisory workflows. In this episode, we explore: Bridging the Team Divide: How to align AI enthusiasts and detractors to move the entire firm forward together. The Enterprise Security Advantage: Why staying within Microsoft Copilot or Google Gemini ecosystems solves the PII (Personally Identifiable Information) and redaction hurdles. The Danger of Adobe Redaction: Why blacking out text in PDFs isn't enough to hide underlying metadata from AI models. Mapping Workflows Backward: Why defining the desired outcome before selecting software leads to better automation. Building a Custom AI "Sales Agent": How to train AI on your unique brand voice to research prospects, analyze websites, and guide discovery calls in real time. The Bleeding Edge: How unified AI awareness layers (like Town.io) aggregate transcripts, email, and task managers into an automated personal assistant. Key AI Tools & Solutions Mentioned: Smart Accountant AI: smartaccountant.ai Enterprise AI: Microsoft Copilot, Google Gemini, and NotebookLM Workflow & Data Aggregation: Town.io, Notion, Asana, and SharePoint Custom AI Prompting: Claude and ChatGPT About Jan Haugo Jan Haugo is a leading AI researcher, trainer, and founder of smartaccountant.ai. She specializes in testing, validating, and deploying practical AI tools for controllership, financial close, and firm operations.   Featured Guest: Jan Haugo  

Would you trust two brothers with zero aviation experience to run El Salvador's helicopter tours, plane charters, and Bitcoin travel experiences? Thousands of Bitcoiners already have. Today on Live From Bitcoin Beach, I sit down with Mauricio and Diego Villeda, founders of Bitcoin Life, the Bitcoin tourism and concierge service company that convinced El Salvador's Minister of Defense to lend them Air Force helicopters. I wanted the full story of how two guys orange-pilled by a $5K Bitcoin client built the most Bitcoin-native travel operation on the planet, and they didn't hold back.It all started with a free Notion page built to help Bitcoiners travel El Salvador, covering transportation, places to stay, and spots that accept Bitcoin over the Lightning Network. Three days after launch, Stacy Herbert retweeted it, and strangers at the Adopting Bitcoin conference were showing the brothers their own guide without knowing who made it. That one retweet turned a side project into a company, and it proved something maxis have said for years, that proof of work beats marketing budgets.These days the brothers run helicopter tours to places most tourists never see, including Lake Ilopango, a lake that had zero tourism when gangs controlled the area. They fly clients beyond El Zonte, land in soccer fields, and pay the local caretakers in sats to clear the space. For Bitcoiners flying in from abroad, they also run VIP airport pickups that take you from immigration straight to a helicopter, plane charters connecting El Salvador with Próspera in Honduras for around $3,000 per group, and real estate guidance for investors front-running the nation-state adoption wave. Every booking, vendor, and ticket runs on Bitcoin, feeding the Bitcoin circular economy that fiat skeptics love to claim doesn't exist.Their biggest project is still ahead, a Bitcoin Street in the Centro Histórico of San Salvador where every business accepts Bitcoin, complete with an academy, an art gallery, and rooftop coffee shops. The brothers are raising from investors before Bitcoin Histórico this November, and I love their case for it. A district that was too dangerous to visit a few years ago deserves a permanent Bitcoin landmark, and low time preference built this country's turnaround.If you've ever wanted to see El Salvador from a military helicopter while paying in sats, these are your guys. Subscribe for more stories from the ground, share this with your favorite nocoiner, and drop a comment telling me which tour you'd book first.—Bitcoin Beach TeamLearn more about The Bitcoin Life:X: https://x.com/thebitcoinlife_ X (Diego Villeda): https://x.com/DiegoVilledaa   X (Mauricio Villeda): https://x.com/itsMauriVilleda Instagram: https://www.instagram.com/thebitcoinlife_ Website: https://www.bitcoinlife.sv/ WhatsApp: +503-6865-6540Support and follow Bitcoin Beach:X: https://www.twitter.com/BitcoinBeach IG: https://www.instagram.com/bitcoinbeach_sv TikTok: https://www.tiktok.com/@livefrombitcoinbeach Web: https://www.bitcoinbeach.com STAY AT BITCOIN BEACH: https://www.stayatbitcoinbeach.com/punta-mango-villasBrowse through this quick guide to learn more about the episode:00:00 Intro01:36 How to get started with Bitcoin as an entrepreneur in El Salvador?04:36 What was the first Bitcoin meetup in San Salvador like?07:48 How to build a Bitcoin travel guide that goes viral?11:42 How to book a helicopter tour in El Salvador with Bitcoin?14:20 Is Lake Ilopango safe to visit now?19:27 How does VIP airport pickup work in El Salvador?21:31 How much does a private flight from El Salvador to Próspera cost?23:24 What is Bitcoin Street in San Salvador?29:51 Where can you spend Bitcoin in El Salvador? Live From Bitcoin Beach

Patrick Renna (The Sandlot, The Big Green, Dugout Dads) joins us this week for a warm and surprisingly raw conversation about carrying a movie three generations have loved since he was thirteen years old. Patrick gets into the almost ridiculous way he landed The Sandlot, why the you play ball like a girl scene was never written for him, and why he will not perform the line everybody asks him for. We also get into the single negative comment that can undo fifty good ones, the money reality of making scale on a classic, and the moment he nearly cried talking about what he hopes his kids remember about him. Thank you to our sponsors:

CJ sits down with Rebecca Schwartz of Tabs, Fred Havemeyer of Fleet AI, and Rahul Rekhi of Rogo for a live conversation about what it actually takes to build an AI-native company. —SPONSORS:RightRev is a revenue recognition platform built for the AI economy, helping finance support usage-based pricing, credits, hybrid contracts, seats plus consumption, and whatever commercial model comes next. It gives product teams the freedom to keep innovating without outdated revenue systems slowing them down. Learn how RightRev can help at https://rightrev.com/cjPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer posts 98% of transactions directly to its ERP, with the remaining 2% routed to a human for review. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered workflows that enforce expense policies at the point of sale, match receipts automatically, and reduce month-end close from weeks to hours. Thousands of companies, including Anthropic, Coinbase, and DoorDash, already run on Brex. Stop asking A-level finance talent to do B-level admin work. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNhttps://www.tabs.com/https://fleetai.com/https://www.rogo.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro1:16 Welcome to Run the Numbers3:17 Meet the Panel: Rebecca, Fred, Rahul9:52 How Rogo Found PMF13:47 How Tabs Found PMF14:51 Growth Rates: Headcount and Revenue16:23 The Real Hiring Constraint17:21 What "Forward Deployed" Actually Means18:53 What's Blocking AI Adoption Now20:19 How Sales Hiring Has Changed24:34 How Go-to-Market Has Changed25:28 Pricing Models: Then vs. Now28:18 Tabs' Pricing Evolution29:20 What the Finance Team Looks Like31:00 How Far You Can Push Outsourced Accounting32:43 Managing AI Gross Margins35:07 The North Star Metric for AI Companies37:02 Token Spend vs. Per-Seat Pricing39:34 Asking Better Questions: What Counts as ARR41:38 Biggest Bug in Cash to Collections43:05 Closing Advice for Founders

It's been a while since we checked in on note-taking apps and ways to wrangle their data, and a lot has changed with our personal use cases on that front. Will has gone deep on Notion's relational capabilities and seen how well they can support a shared knowledge base for a team of a couple dozen people. Meanwhile, Brad has done a survey of apps targeted at the individual end user, with the goal of fully owning the data and controlling how and where it's synced. We round up everything we've discovered in this week's episode, with some detours into things like rclone cloud mounts, the amazing utility of the (nearly) universal format conversion tool pandoc, and more. Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Link to the Notion doc:https://app.notion.com/p/superwall/App-Masters-Podcast-3c39c6f344178086becce9df17c27979 The subscription app landscape is evolving faster than ever.In this episode, Nick Godwin, Product & Customer Growth at Superwall, shares what the highest-performing subscription apps are doing differently in 2026. From winning paywalls and onboarding flows to AI agents, experimentation, and monetization, you'll learn the strategies helping apps grow faster and generate more subscription revenue.He'll also share how AI is changing the way apps are built, personalized, and optimized, why experimentation has become a competitive advantage, and the biggest lessons Nick has learned working with thousands of subscription apps.If you're building or growing a subscription app, this episode is packed with practical insights you can apply today.You will discover:✅ How the best subscription apps approach paywalls in 2026.✅ Winning onboarding and monetization strategies.✅ Why experimentation is the key to sustainable growth.✅ How AI agents will reshape subscription apps.✅ The biggest trends every app founder should know.Learn More:Superwall: https://superwall.com Superwall YouTube: https://youtube.com/@SuperwallHQ Superwall Events: https://luma.com/superwallHQ

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

Thank you to Notion for sponsoring this episode! Go to http://www.notion.com/kitandkrysta and try out Notion today!Thank you to CashApp for sponsoring this episode! Download Cash App Today: https://capl.onelink.me/vFut/vtl4t6ru Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Direct Deposit, Overdraft Coverage and Discounts provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast  for full disclosures.*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*Hello and welcome to episode 236 of the Kit & Krysta Podcast! We have another amazing guest this week - Kevin Bayliss. Kevin is a former Rare artist/character designer and is the person that designed Donkey Kong for Donkey Kong Country and he also created Diddy Kong! WOW! Kevin has so many amazing stories to share about his time at Rare and we can't wait for you all to hear it. Also in this episode, there are even more Zelda 40th Anniversary leaks and huge news for Kingdom Hearts 4. All this and more is coming right up. 0:00 - Special Guest month continues13:54 - Welcome Kevin Bayliss1:21:48 - News news news1:57:01 - Games we're playingFollow Us! https://www.patreon.com/kitandkrystahttps://twitter.com/kitandkrystahttps://www.tiktok.com/@kitandkrystahttps://www.instagram.com/kitandkrysta/http://www.facebook.com/kitandkrysta/https://bsky.app/profile/kitandkrysta.bsky.social-Kit & Krysta

In this episode of Run the Numbers, CJ sits down with Wisam Hirzalla, Head of Product at Stripe Billing, to trace the full journey of a dollar—from product packaging and pricing through quoting, contracts, billing, tax, collections, and revenue recognition.—SPONSORS:Anrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is a revenue recognition platform built for the AI economy, helping finance support usage-based pricing, credits, hybrid contracts, seats plus consumption, and whatever commercial model comes next. It gives product teams the freedom to keep innovating without outdated revenue systems slowing them down. Learn how RightRev can help at https://rightrev.com/cjPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer posts 98% of transactions directly to its ERP, with the remaining 2% routed to a human for review. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered workflows that enforce expense policies at the point of sale, match receipts automatically, and reduce month-end close from weeks to hours. Thousands of companies, including Anthropic, Coinbase, and DoorDash, already run on Brex. Stop asking A-level finance talent to do B-level admin work. Learn more at https://www.brex.com/metricsEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/wisam-hirzalla-9b20a91/Company: https://stripe.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 What Drives Billing Downstream1:41 Welcome to Run the Numbers2:37 Wisam's Path to "Queen of Billing"4:55 What Head of Billing at Stripe Entails5:45 Defining Quote-to-Cash7:50 Layer 1: Pricing and Packaging Mistakes14:46 Pricing Models Rising Because of AI18:11 Layer 2: Inside a Quote19:27 Why Enterprise Deals Get So Messy24:38 Layer 3: Contracting27:21 Layer 4: Billing30:15 Why Usage-Based Billing Is So Complex34:51 Tokens as a Pricing Unit36:57 Billing's Move Out of the ERP39:53 Layer 5: Provisioning and Entitlements43:36 Layer 6: Tax44:27 Layer 7: Getting Paid48:18 Layer 8: RevRec50:52 When Billing Stopped Being Back Office51:47 Asking Better Questions Segment52:28 Will AI Replace Finance Jobs

Stephen and Chris hop on to talk about the couple of weeks post 2.0 launch. Things are certainly different in 2.0! Literally the location of certain buttons is in a new place, and that requires folks to poke around a bit and figure it out. Some people just do that and it's a non-issue. Some write in for help and even find delight in the new features. Some lash out. The cheese metaphor is from an old book by Spencer Johnson about people experiencing change in the workplace and learning to deal with it. You might call it a bit heavy handed, and the metaphor was plucked away by UX folks to describe interface changes, so consider this the end of the line for this already-stretched metaphor. That didn't stop us from using the icon in our documentation about this though. Sponsor: Notion With the recent launch of Custom Agents, Notion became the collaborative AI workspace where teams and agents work side by side. And now, their new Developer Platform is turning that workspace into infrastructure developers can build on. Time Jumps

AI agents now autonomously execute full SEO workflows, not just tasks. Thenuka Karunaratne, CEO and co-founder of Sunbeam and Daydream, raised $20M+ building an AI-native SEO agency serving Notion, Replit, and Clay before launching Sunbeam, an autonomous SEO agent that has moved beyond workflow automation. Karunaratne breaks down why proactive "teammate" agents outperform reactive workflow-based tools, how leading indicators shift from indexation to ranking velocity to conversion as agent-driven programs mature, and why domain authority remains resistant to automation while on-site technical and content execution increasingly isn't. The conversation maps how SEO practitioners must shift from tactical execution toward product-fluent strategic oversight as autonomous agents absorb research, technical fixes, and content production at scale.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Front CFO & COO Meredith Finn joins CJ to explain why finance leaders are taking on more of the operator's job. They break down the CFO-COO dual mandate, how Front funds new bets inside a mature SaaS business, why AI requires more coordination than simply handing everyone new tools, and how finance teams should think about the growing cost of AI.—SPONSORS:Brex is an intelligent finance platform with AI-powered workflows that enforce expense policies at the point of sale, match receipts automatically, and reduce month-end close from weeks to hours. Thousands of companies, including Anthropic, Coinbase, and DoorDash, already run on Brex. Stop asking A-level finance talent to do B-level admin work. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is a revenue recognition platform built for the AI economy, helping finance support usage-based pricing, credits, hybrid contracts, seats plus consumption, and whatever commercial model comes next. It gives product teams the freedom to keep innovating without outdated revenue systems slowing them down. Learn how RightRev can help at https://rightrev.com/cjPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer posts 98% of transactions directly to its ERP, with the remaining 2% routed to a human for review. You pay for outcomes, not seats. See it at https://www.maximor.ai/—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/meredithfinn1/Company: https://front.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Adam Swiecicki - CFO of Ripplinghttps://youtu.be/JyGJVpmZNacAurélien Nolf - CFO of Navanhttps://youtu.be/siwzJSRXAvY—TIMESTAMPS:0:00 The CFO/COO Dual Mandate1:45 Welcome to Run the Numbers2:52 Front's In-House Podcast Studio3:41 Meredith's Investment Thesis on Front7:21 The "Portfolio Career" Analogy8:35 What She Had to Unlearn as an Operator13:13 Books on Strategy vs. Execution16:17 When a CFO Should Also Be COO18:27 What the COO Hat Actually Covers20:20 Where the Two Roles Conflict25:42 Budgeting for AI Token Costs29:16 Using Internal Usage as a Benchmark35:40 Funding a Series A Bet Inside a Series D Company38:04 Sizing Bets: Core, Adjacent, Moonshot40:04 The Secret to Re-Accelerating Growth41:46 Going Deeper, Not Wider43:18 What the Pricing Overhaul Taught Her47:30 Why Customer P&L Matters50:11 Lightning Round Begins51:32 Advice to Her Younger Self52:33 Front's Finance Tool Stack53:21 Craziest Expense Ever#RunTheNumbersPodcast #CFOLife #FinanceLeadership #AIinFinance #SaaSGrowth

Ian Silber is the head of product design at OpenAI, where he has led the design of ChatGPT, Codex, and all of OpenAI's product experience for the past three years. Before OpenAI, he was at Artifact, the AI-powered news app built by the founders of Instagram. Prior to that, he spent eight years at Instagram, where he worked on products including Reels. Ian is one of the most consequential designers working in AI today, and he takes us inside how OpenAI designs ChatGPT, Codex, and the future of how we will interact with AI.In our in-depth conversation, we discuss:1. Why Ian believes this is the best time in history to be a product designer2. Why engineers 10x'd with AI but design teams haven't3. What OpenAI looks for when hiring designers4. “Just do less”: Ian's counterintuitive advice to his designers5. The future of ChatGPT as a super app6. Where humans still win: user understanding, invention, and point of view—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and moreMercury—Radically different banking, now with Command—Where to find Ian Silber:• X: https://x.com/iansilber• LinkedIn: https://www.linkedin.com/in/iansilber• Website: https://iansilber.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Ian Silber(02:14) Why designers in general feel anxious about AI(09:01) What makes specific designers thrive in the AI era(13:41) Why Ian says it's the best time in history to be a designer(17:13) How product roles are converging(22:24) Can AI design great products?(23:54) Where human judgment still matters(27:34) What Ian looks for when hiring designers(30:20) Why systems thinking matters(32:53) Balancing speed and craft(38:05) Designing for vastly different audiences(41:57) Solving the blank-box problem(43:31) How ChatGPT is evolving beyond chat(46:07) The vision for Codex(49:07) What Ian wishes he knew on day one(51:40) Why humility matters in AI(53:41) Advice for designers who are feeling overwhelmed(55:16) AI corner(57:43) Failure corner(01:00:45) Lightning round and final thoughts(01:05:52) Lessons from Groupon—Referenced:• OpenAI: https://openai.com• How tech workers are feeling in 2026: a workforce splitting in two: https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• 3 Spiderman Pointing meme template: https://www.kapwing.com/explore/3-spiderman-pointing-meme-template• OpenAI Codex lead on the new shape of product work | Andrew Ambrosino: https://www.lennysnewsletter.com/p/openai-codex-lead-on-the-new-shape• Notion: https://www.notion.com• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• Joel Lewenstein on LinkedIn: https://www.linkedin.com/in/joel-lewenstein• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• ChatGPT Work: https://openai.com/chatgpt-work• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Please Stop the AI Confidence Theater: https://www.elenaverna.com/p/please-stop-the-ai-confidence-theater• The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth): https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna• Maybe Happy Ending: https://www.maybehappyending.com• The Invite: https://www.imdb.com/title/tt14173636• Rivian: https://rivian.com• Waymo: https://waymo.com• Groupon: https://www.groupon.com• How a VC and a tech founder used AI to launch a brick-and-mortar business in their spare time | Andrew Mason (CEO of Descript) and Nabeel Hyatt (General Partner at Spark Capital): https://www.lennysnewsletter.com/p/how-a-vc-and-a-tech-founder-used• Andrew Mason on X: https://x.com/andrewmason• Kevin Systrom on LinkedIn: https://www.linkedin.com/in/kevinsystrom• Sam Altman on X: https://x.com/sama—Recommended book:• The Design of Everyday Things: https://www.amazon.com/dp/0465050654—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

C'est peut-être un détail pour vous… mais pour moi, ça veut dire beaucoup.Un nouveau rendez-vous de La Cohorte où l'on revient sur des phrases entendues dans les interviews du podcast. Des phrases qu'on aurait tort de laisser filer car elles révèlent beaucoup sur la manière de construire son business freelance!Aujourd'hui, je me replonge dans l'interview de Lucas, spécialiste Notion, qui nous a expliqué comment il gamifie son quotidien d'entrepreneur.Je reviens sur trois passages qui méritent qu'on s'y arrête :– quand Lucas décrit comment il s'envoie des notifications automatisées complètement loufoques dans Notion, pour transformer les tâches qui le saoulaient en petits moments de joie,– quand il explique pourquoi il préfère se fixer des objectifs mensuels réalistes — qu'il appelle des "quêtes" — plutôt qu'ambitieux à tout prix, et comment ça le maintient motivé mois après mois,– et quand il raconte comment il mesure non seulement ses résultats business, mais aussi son bien-être, avec ce qu'il appelle ses "kiwis" : des Key Wellbeing Indicators.Trois détails qui rappellent une chose simple : en solo, personne ne passe vérifier si tu vas bien au boulot.Pas de manager, pas de RH. C'est à toi de jouer ce rôle.Et toi, mon brin de romarin : est-ce tu te te marres suffisamment au boulot?(Pour me répondre, envoie-moi un mp sur Linkedin

In this episode of Run the Numbers, CJ Gustafson sits down with Xero CFO Claire Bramley to discuss how the CFO role is expanding beyond finance into strategy, operations, transformation, and technology. Claire also shares how she builds teams around her weaknesses, what small businesses actually want from AI, and why she views diversity as a business performance decision.—SPONSORS:Maximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer posts 98% of transactions directly to its ERP, with the remaining 2% routed to a human for review. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered workflows that enforce expense policies at the point of sale, match receipts automatically, and reduce month-end close from weeks to hours. Thousands of companies, including Anthropic, Coinbase, and DoorDash, already run on Brex. Stop asking A-level finance talent to do B-level admin work. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is a revenue recognition platform built for the AI economy, helping finance support usage-based pricing, credits, hybrid contracts, seats plus consumption, and whatever commercial model comes next. It gives product teams the freedom to keep innovating without outdated revenue systems slowing them down. Learn how RightRev can help at https://rightrev.com/cjPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/claire-bramley-133b7174/Company: https://www.xero.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro3:00 Xero and CJ's History With It4:12 Xero's Size and Scale Today5:10 The Expanding CFO Role7:06 Hiring Experts You're Not8:47 Imposter Syndrome11:00 Getting Better at Asking Questions15:04 Asking the "Stupid Question"16:21 Finance as the Corporate Junk Drawer17:48 The Trial Period Idea18:24 Tripwires: Leading Indicators for CFOs25:35 AI Hype vs. Main Street Reality26:44 What Small Business Owners Actually Want28:45 What Big Corps Underestimate31:10 Trust as a Competitive Moat33:45 Why the Line Is Thinner for Small Biz35:12 AI Inside Claire's Finance Team37:23 AI for Investor Relations39:28 Diversity as Capital Allocation42:07 A Decision Diversity Changed44:45 Lightning Round Begins46:06 Advice to Her Younger Self48:49 Craziest Expense Ever#RunTheNumbersPodcast #CFOLife #FinanceLeadership #AIinFinance #WomenInFinance

Okay, so if you have been wondering whether this podcast quietly died, I do not blame you.It has been quiet for an embarrassingly long time. No episodes, no emails, no blog posts, literally nothing. And I could hand you some cute little reason wrapped in a bow, but that is not how I do things around here.So here is the truth. The last year of my life came apart. Not in a dramatic movie way, but in a slow, one thing after another way. We sold our house, the living situation we moved into did not work out, and I packed up my son and left with a car, a kid, and a very big problem. We landed in Ohio, which solved the roof over our heads and created a much bigger problem for my business, because my mom, who had my son two days a week, was now 600 miles away.That was over 40 hours a week gone basically overnight. And Boss Lady Bloggers went dark right along with it.But I am back, and I am better for it. Not in a fake everything happens for a reason way. Better because this year taught me something about building a business that I could not have learned any other way.In this episode:Why a business does not die in one loud dramatic moment, it just gets softer and softer until you look up and realize you have not published anything in monthsThe full story of the year, including the part nobody expects, which is that falling in love with teaching toddlers was not the sad partThe three things the quiet year actually taught meWhy that calm season you are waiting for is not coming, and what to do insteadMy confession about being really, really good at building systems when I am scared to do the actual work. ClickUp, Notion, Capacities, Apple Notes, and then right back around againWhy giving up is usually not quitting, it is waitingThis one is for you if you are trying to build a blog or a business or an offer inside a life that is already full and already messy and already not cooperating.Next episode I get specific. The exact plan for rebuilding this business around a full time job, a kid, and a full course load. What I am keeping, what I am killing, and the order I am doing it in.Mentioned in this episode:My new site, which is honestly the best one I have built for Boss Lady Bloggers so far → bossladybloggers.comThe free course library. Six blogging courses I put out for free, and it is not going to be free forever → https://bossladybloggers.com/freebiesWant your homepage redesigned for free? Fill in the pop-up on my site and I will show you what is possible. Not a homepage that is just a pile of blog posts. A gorgeous site with copy designed to sell whatever you want to sell this year. Send me a message. Tell me what you are building and what is standing in your way. hello@bossladybloggers.com. I read them, and honestly, after this year I could use the company too.

Send us Fan MailA wheelchair, a notebook flipped over in a failing math class, and one contest win that changed everything. That's the origin story Tracee Garner brings to our mic, and it opens into a bigger conversation about disability advocacy, mental health, and the kind of access that lets people actually live their lives.Tracee is a disability advocate in Northern Virginia and a published author with 22 books, and she's refreshingly honest about what it takes to keep creating while living with muscular dystrophy. We talk about how community college became the right-sized environment to rebuild confidence, how writing can carry you through depression and stress, and why journaling does not have to mean pen and paper. Tracee shares how tools like Notion and dictation support her day-to-day as her hand strength changes, and why flexible systems matter more than “perfect” routines.We also get into the policy side of disability rights: finding your voice, learning to deliver a clear message to decision-makers, and pushing for basics like transportation, accessibility, and caregiver support. From automatic doors to accessible stalls, we explore the truth that inclusive design usually helps everyone, even the people who resisted it. Then we zoom in on remote work accommodations, what the pandemic shifted, and why accessibility at work still has a long way to go.Finally, Tracee walks us through her fiction, her love of backstory and triumph, and what's coming next, including “Gather” and “This Too Is Life,” a collection of essays by authors with disabilities timed for October awareness. If you care about accessibility, assistive technology, inclusive workplaces, or creative resilience, hit play, subscribe, share this with a friend, and leave a review so more people can find the show.Support the showSJ CHILDS - SOCIALS & WEBSITE MASTER LISTWEBSITES- Stream-Able Live — https://www.streamable.live-COMING SOON- The SJ Childs Global Network — https://www.sjchilds.org- The SJ Childs Show Podcast Page — https://www.sjchildsshow.comYOUTUBE- The SJ Childs Show — https://www.youtube.com/@sjchildsshow- Louie Lou (Cats Channel) — https://www.youtube.com/@2catslouielouFACEBOOK- Personal Profile — https://www.facebook.com/sara.gullihur.bradford- Business Page — https://www.facebook.com/sjchildsllc- The SJ Childs Global Network — https://www.facebook.com/sjchildsglobalnetwork- The SJ Childs Show — https://www.facebook.com/SJChildsShowINSTAGRAM- https://www.instagram.com/sjchildsllc/TIKTOK- https://www.tiktok.com/@sjchildsllcLINKEDIN- https://www.linkedin.com/in/sjchilds/PODCAST PLATFORMS- Spotify — https://open.spotify.com/show/4qgD3ZMOB2unfPxqacu3cC- Apple Podcasts — https://podcasts.apple.com/us/podcast/the-sj-childs-show/id1548143291CONTACT EMAIL- sjchildsllc@gmail.com

Ian and Aaron discuss Aaron's first week as VP of Marketing & Community at Laravel, why he's writing manifestos in Notion, Ian's huge week with AI coding, and an update on Nachogate.Plus Ian's live from a cabana, ending Token Town (RIP to a real one), and so much more.Sponsored by Bento, DropInBlog, Valorin Security, and Svix.Interested in sponsoring Mostly Technical?  Head to https://mostlytechnical.com/sponsor to learn more.(00:00) - Live from a cabana (03:26) - The End of Token Town (10:39) - Aaron's First Week (28:03) - A few manifestos (44:24) - 1:1's (53:40) - Ian's Mega AI Coding Week (01:10:21) - Nachogate Update (01:12:43) - Aaron's Studio Update (01:18:25) - The next Illustrated Classics Links:Aaron's Notion setupTaylor's tweet about TrelloRands In Repose: The Update, The Vent, and The Disaster John O'Nolan's tweet indicating some discrepancies with the nacho storyGreat Illustrated Classics

En sept ans, j'ai publié plus de 600 épisodes de podcast et plus de 2 000 posts Instagram. J'ai une base Notion avec plus de 300 idées de contenus et....  comme tout le monde il m'arrive encore d'ouvrir une page blanche en me demandant ce que je vais bien pouvoir raconter aujourd'hui. Sauf qu'en réalité, ça n'a jamais été un problème d'idées.Dans cet épisode de "J'peux pas j'ai business", je vous montre les sept réflexes qui font que je publie quand même quand tous les jours, même quand j'ai l'impression de n'avoir rien d'intéressant à dire ou que je manque d'idées de contenus. On parle de :

Show DescriptionWe tackle a listener question about why junior front-end devs are expected to know so much beyond HTML and CSS, then get into Dave's real-world fix swapping old WebKit line-clamp hacks for the new CSS line-clamp property and the subtle bugs that kind of truncation work can cause. They also debate whether AI tools like Claude or Copilot deserve co-author credit on commits, gripe about agent-only coding workflows that skip linting and hide what's actually happening, and wrap up looking at new platform features like the navigation API and a proposed CSS-based routing approach using @route and @view-transition. Listen on WebsiteWatch on YouTubeSponsorsNotionWrite custom tools for Notion Agents that generate assets, query live data, and hit any API. Listen for incoming webhooks from any app, then run workflows with Notion Agents, pages, databases, and external APIs. All of this, on a hosted runtime. Workers are isolated sandboxes managed by Notion, so the code behind your syncs, tools, and workflows runs on our infra instead of your servers.

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstWhy can deep networks discover abstractions that shallow models miss? Statistical physicist Matthieu Wyart joins Tim Scarfe to argue that the answer lies in the hidden hierarchy of data. Language and images are built from parts within parts; depth lets a network recover those coarse-grained variables and escape the curse of dimensionality.The conversation moves from jamming transitions and rough loss surfaces to Chomsky, context-free grammars and machine creativity. Wyart explains why next-token prediction can still recover compositional structure, where current systems fall short of genuine scientific invention, and why predicting latent representations rather than raw tokens could make learning far more sample-efficient.They also examine diffusion models, neural scaling laws and the limits of physics-inspired theory. The final question is on a personal note: if mistakes are the price of leaving the beaten path, how much scientific risk is worth taking?---TIMESTAMPS:00:00:00 Can machines learn abstractions from data?00:02:00 Notion agentic workspace00:02:49 From statistical physics to machine learning00:06:40 What physics can explain about learning00:16:37 From Carnot to Chomsky bulldozer00:21:21 How deep networks recover hidden hierarchies00:32:43 Where machine creativity still falls short00:40:48 How deep nets escape the curse of dimensionality00:52:19 Why predict latents instead of tokens01:02:49 The sample-efficiency case for latent prediction01:08:31 Diffusion, scaling laws and text entropy01:16:40 The scientists we learn from and the mistakes we make---REFERENCES:person:[00:00:43] Noam Chomskyhttps://linguistics.mit.edu/user/chomsky/tool:[00:02:08] Notion Developer Platformhttps://www.notion.com/en-gb/blog/introducing-developer-platformpaper:[00:04:43] Mastering the game of Go with deep neural networks and tree searchhttps://www.nature.com/articles/nature16961[00:05:52] Reconciling modern machine-learning practice and the bias-variance trade-offhttps://arxiv.org/abs/1812.11118[00:25:54] How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Modelhttps://arxiv.org/abs/2307.02129[00:42:12] Efficient Estimation of Word Representations in Vector Spacehttps://arxiv.org/abs/1301.3781[00:52:46] Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturehttps://arxiv.org/abs/2301.08243[00:52:54] Learn from your own latents and not from tokens: A sample-complexity theoryhttps://arxiv.org/abs/2605.27734[01:08:31] A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Datahttps://arxiv.org/abs/2402.16991[01:11:39] Scaling Laws for Neural Language Modelshttps://arxiv.org/abs/2001.08361[01:12:17] Deriving Neural Scaling Laws from the statistics of natural languagehttps://arxiv.org/abs/2602.07488[01:13:34] Prediction and Entropy of Printed Englishhttps://ieeexplore.ieee.org/document/6773263---LINKS:Download PDF transcript: https://app.rescript.info/share/f7644cdaa86c5cc1e41e484e290f2bd4

On this episode of Run the Numbers, CJ and Ben trace corporate private jets from Sam Walton's scrappy two-seater to Alex Karp's $17 million jet bill. They break down when flying private actually makes business sense, how jets hit the balance sheet, the tax and accounting implications, what companies disclose to investors, and when executive travel crosses the line from productivity tool to corporate excess.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer posts 98% of transactions directly to its ERP, with the remaining 2% routed to a human for review. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered workflows that enforce expense policies at the point of sale, match receipts automatically, and reduce month-end close from weeks to hours. Thousands of companies, including Anthropic, Coinbase, and DoorDash, already run on Brex. Stop asking A-level finance talent to do B-level admin work. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is a revenue recognition platform built for the AI economy, helping finance support usage-based pricing, credits, hybrid contracts, seats plus consumption, and whatever commercial model comes next. It gives product teams the freedom to keep innovating without outdated revenue systems slowing them down. Learn how RightRev can help at https://rightrev.com/cjPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Jersey Mike's IPO: The $4.2B Sandwich Machinehttps://youtu.be/ZL6AbqHI-0g—TIMESTAMPS:0:00 Fly Like a G60:29 $17M on jets: Alex Karp guess1:16 Sam Walton starts it all2:49 The yellow legal pads4:37 Walmart Aviation founded (1968)5:14 Piston to Bombardier jets6:51 Walmart's modern jet fleet8:13 Where it hides in SEC filings9:25 SEC Reg S-K Item 40213:37 Gulfport Energy scandal14:42 Tyco: Kozlowski & Schwartz16:57 When jets are actually useful17:55 P&L vs. balance sheet treatment18:40 PP&E and depreciation basics19:04 Off-balance-sheet leases19:28 The chartering loophole19:54 "Uber Jet" fractional ownership23:14 How activists hunt jet spend23:36 Flight registries & tail numbers24:13 Jack Sweeney vs. Elon Musk25:00 What top companies spend25:18 Walmart's fleet is #126:17 Charter vs. lease vs. own29:02 Should we care? 3 angles30:06 Angle 2: opportunity cost30:59 Angle 3: company size31:42 General Electric's shadow jet32:50 Back to Alex Karp34:02 Credits#RunTheNumbersPodcast #PrivateJets #CorporateFinance #ExecutivePerks #CFOLife

As a companion piece to our Bugs Bunny Superstar episode, Marc and Jordan cover three shorts that Bob Clampett is, technically, credited on...that have some dubious production histories. 'Birth of a Notion' was started by Bob, and expertly finished by Robert McKimson's unit. 'Bacall to Arms' was started by Bob, and was given to Arthur Davis's unit without a clear gameplan. And 'Porky's Badtime Story' was started by Ub Iwerks, and given to Bob and Chuck Jones, without warning, to finish it. There's some very good stuff in here, but while some ideas feel seamless, other feel completely incongruent. Links:Support us on PatreonFollow us on TwitterFollow us on BlueskyFollow us on Instagram

Dan doesn't usually sit in the interviewee's chair — but when journalist and author Allison Gilbert invited him to speak at the 92nd Street Y as part of their Connected Lives series, he said yes. What followed was one of the more wide-ranging conversations Dan has had about his own practice, his own anxieties, and why he's become convinced that inner work divorced from relationships is just selfishness dressed up as self-care. The evening also included a guided loving-kindness meditation — metta practice — that Dan led live with the audience. They talked about: Why Dan thinks the meditation world's PR problem has shifted — it's no longer "this is weird hippie stuff," it's "I get it, but I personally can't do it" The upward spiral: how taking care of your mind improves your relationships, which improves your inner weather, which improves your relationships further — and the "toilet vortex" that runs in the opposite direction Free-range mindfulness: why brushing your teeth or standing in a grocery line can rewire your brain just like formal seated practice The self-compassion toolkit — Kristin Neff's three-step practice, distanced self-talk, and why putting your hand on your heart is "cheesy as f*ck" but actually works Joseph Goldstein's single best question for breaking an anxiety loop: "Is this useful?" Why micro-interactions — with your doorman, your barista, a stranger on the elevator — are one of the most underrated sources of happiness Allison Gilbert is a journalist, author, and the creator of The Joy of Connections, a book and companion app designed to help people build more meaningful relationships. She writes and speaks about human connection, grief, and resilience. Learn more at allisongilbert.com. Get the 10% with Dan Harris app here Sign up for Dan's free newsletter here Follow Dan on social: Instagram, TikTok Subscribe to our YouTube Channel Additional Resources:  Check out Allison's new app at beingmoreconnected.com This episode is sponsored by: Notion: Learn more about how Notion can support your business, at notion.com/happier. To advertise on the show, contact sales@advertisecast.com or visit https://advertising.libsyn.com/10HappierwithDanHarris

I recorded this one live on the last day of week two of my birthday sabbatical, and it published about ten minutes after I stopped talking. Because I spent the last seven days — on roughly four hours of sleep a night — building a custom client portal that replaced Airtable, Fillout, Notion, Google Docs, and Google Sheets in my business.What started as "let me move my AI assistants out of ChatGPT" turned into a full app where my clients log in, map their client journey, draft their emails, submit support tickets, send me voice notes, and do their weekly check-ins. I had a fully deployed version in 12 hours after telling myself since February that I needed time to plan it.I also knocked South Dakota off my list (47 states down, 3 to go), and I get into the irony of doing the exact thing I tell y'all not to do with your own systems.Demo and debrief coming to the blog soon. Get on the list so you don't miss it: coliejames.com/subscribeWork with me: Systems in Session | The Experience Edit

Benjamin and Chance return for another episode recapping the week in Apple news, including the interesting details of Apple's most recent earnings calls, the last of the Tim Cook era. Also, Apple complies with Microsoft's Interoperability request for universal clipboard support on Windows, and John Ternus recruits an employee out of retirement to help lead his new company regime. And in Happy Hour Plus, Chance is battling with HomeKit reliability woes, and ponders whether Benjamin's messaging habits. Subscribe at 9to5mac.com/join. Sponsored by Bitwarden: Check out Bitwarden, featuring secure password management, passkey support, end-to-end encryption, and seamless autofill across all devices. Sponsored by Notion: Learn more about how Notion can support your business, at notion.com/happyhour. Sponsored by Square: Get up to $200 off Square hardware when you sign up at square.com/go/happyhour. Sponsored by NordStellar: Get an exclusive offer: Unlock your 10% discount on NordStellar with the coupon code: nordhappyhour-10-NORDSTELLAR – just mention it to NordStellar!

Elon Musk's SpaceX is spending billions on AI data centres, rocket ships and moon ambitions but investors are asking how much it can burn before the bet pays off. Meanwhile, ‘AI slop' is flooding social media. And new EU rules mean platforms will have to label AI-generated content. But can regulation keep up? Danny Fortson and Mark Sellman discuss SpaceX, Starlink, space junk and the growing fight over AI content online. Plus, Danny speaks to Ivan Zhao, co-founder and CEO of Notion, about AI agents, the future of work and why his company hired a 16-year-old high schooler. Watch on YouTube Read more: Authors must prove their work is not AIProducer: Harry Bligh, Marnie Duke, Shabnam Grewal Executive Producer: Priyanka DeladiaVideo Producer: Bronwen LathamImage: Getty Hosted on Acast. See acast.com/privacy for more information.

On this episode of Run the Numbers, CJ Gustafson sits down with Richard Haram, VP of Finance at RapidSOS, to unpack how finance supports a public safety AI platform that helps 911 operators and first responders act faster. They cover building AI fluency, combining FP&A with BI, monetizing vertical software, integrating acquisitions, and the leadership lessons Richard carried from entrepreneurship.—SPONSORS:Pulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer posts 98% of transactions directly to its ERP, with the remaining 2% routed to a human for review. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered workflows that enforce expense policies at the point of sale, match receipts automatically, and reduce month-end close from weeks to hours. Thousands of companies, including Anthropic, Coinbase, and DoorDash, already run on Brex. Stop asking A-level finance talent to do B-level admin work. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is a revenue recognition platform built for the AI economy, helping finance support usage-based pricing, credits, hybrid contracts, seats plus consumption, and whatever commercial model comes next. It gives product teams the freedom to keep innovating without outdated revenue systems slowing them down. Learn how RightRev can help at https://rightrev.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/richardharem/Company: https://rapidsos.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro3:10 What RapidSOS does4:59 CJ's own car accident5:33 What's broken about 9116:50 Who's the actual customer7:46 How RapidSOS monetizes10:13 Sponsors — Pulley | Rillet | Maximor13:27 Too much data, too much noise16:35 Data pollution and trust18:24 The data stack early on19:25 Should BI have come sooner?20:45 The framework: questions to decisions22:43 Building a data culture23:58 Sponsors — Brex | Anrok | RightRev26:51 Bottoms-up vs. top-down AI28:42 Inspiration, not training32:24 The three-phase AI progression33:31 Founding an AV company at 835:37 Giving people room to grow38:12 Depending on heroes39:55 Working under founder personalities41:23 Do startups undervalue what they sell42:48 A price hike, zero churn43:24 Signs you're underpricing44:57 Lightning round45:03 Screwed up: refreshed data before a demo47:15 Advice to younger self47:41 Finance software stack48:37 Craziest expense: personal gas49:29 Credits

8/5/26Episode SummaryTwo things about AI are true at the same time: it's genuinely powerful when wired into your systems, and getting it there is a lot harder than the demos suggest. In this episode Scott gives you both halves. He walks through Client OS, the tool JadePuma built and runs on client brands — a connector that sits between Shopify, Klaviyo, Notion, GitHub, and Google Drive, holds a detailed brand definition, and routes each task to whichever AI model fits it best by API.The demo covers what's actually running today: automated 404 reports with confidence-scored redirect recommendations, scheduled ADA compliance checks that hand errors to a human expert, brand-compliance scoring on existing pages and emails, sentiment analysis on customer reviews to keep brand language in sync with how customers actually talk, and landing pages built from the theme's existing sections. Coming next: ad platforms, social, Google Analytics, and a connection to the marketing calendar — with the goal of one coordinated campaign across every channel, assembled by AI with human checkpoints built into every skill.Then the honest part. Client OS has taken more than a full-time month from JadePuma's strongest developer and it isn't finished. The connections break, APIs change, and chaining tasks together multiplies the failure points. That math works for an agency amortizing the build across a roster of brands; it usually doesn't work for a single store. Scott's takeaway for store owners: don't build this yourself — ask your agency what they're building, how they're using AI on your brand, and what they'd never let it touch.Show LinksLeave a review - https://ratethispodcast.com/solutionsVideo & Transcripthttps://jadepuma.com/blogs/the-shopify-solutions-podcast/episode-191-what-ai-can-do-for-your-shopify-brand-today

Welcome back to another episode of the Best Kept Secret podcast show with your host-DJ Jon Lockley. This month's episode sees the return of the mighty duo known as BRKN DWN. It's been a while since they graced us with a mix, but just in case you forgot, they return with something special to remind you of their greatness. This time around, BRKN DWN bring us current dancefloor heaters, old favorites, reboots and even an unreleased tune of their own. Be warned! Your speakers will be tested. Enjoy their journey through sound because they prove that it is possilble for a Canadian and an American to get along and produce something wonderful. As always, if you're enjoying what you're hearing, like, comment, and share with your friends. Tracklist:1.       Krafty Kuts-New Ting2.       Destroyers, CODE BREAKERZ-TEKK3.       Anuschika, DJ WAVS-My Love4.       Huda Hudia-On That EOI(Huda VIP Mix)5.       OnDaMike-Dat Fiya6.       NOTION vs Redlight-Get Out My Head(Extended Mix)7.       Face & Book-Chocolate8.       Bubble Couple-Tonight9.       Nokaut-Show Me10.   Fran Break-Greenlight11.   Godfader-Legends12.   Wolfgang Gartner-Push & Rise & Clap(Krisp Luvz Noise Reboot)13.   The All Star Breakers-Break to the Bass14.   OnDaMike-Let's Get Filthy15.   Kid Panel-I Need16.   Specimen A & Kwerk ft MC Shureshock & Jose Rodriguez(Spain)-Proufound17.   Stanton Warriors-Hope Time18.   BRKN DWN-Bomb Tha Bass

Several months ago, I published an episode called “The Most Powerful Journaling Practice I've Ever Experienced.” Since then, I have continued using this practice every day, seven days a week. Over time, it has evolved into something much more structured, valuable, and revealing than I originally understood. In this episode, I share my complete daily logging and reflection process. This practice begins with short audio recordings throughout the day. I normally start when I wake up. Sometimes I simply record the time, where I am, and how I feel. Other mornings, I use the recording as a spoken version of morning pages, allowing my stream of consciousness to come out without editing or organizing it. Throughout the rest of the day, I record short check-ins about things such as: my emotional state my priorities for the day the focused block of work I am entering distractions that pull me away from what I intended to do meaningful events and decisions business and relationship developments insights from coaching conversations unexpected disruptions positive feedback and moments worth remembering the stories being told by what I call my inner narrator These recordings may last ten seconds or several minutes. The point is not to create a perfect record of the day. The purpose is to capture meaningful experiences before they disappear from my conscious awareness. The Problem This Practice Solves Before developing this consistent practice, my evaluation of a day was often disproportionately shaped by what remained unfinished. At the end of the day, my inner narrator would frequently focus on: the newest problem the strongest emotional reaction an unresolved decision an open loop something that did not go according to plan something that still needed to be fixed A day could contain hours of meaningful work, valuable conversations, good decisions, relational connection, and personal growth. Yet one emotionally charged problem late in the afternoon could cause me to conclude that the entire day had gone poorly. Daily logging gives me a more complete set of evidence. Turning the Daily Log Into a Daily Reflection At the end of the day, I select all of the audio recordings and merge them into one file. There may be fifteen to thirty-five individual recordings. I then create a transcript and bring it into ChatGPT. I call my ChatGPT Mira, because I use it as a mirror. Throughout the day, I may already have used the same conversation thread to process emails, decisions, relationship dynamics, business questions, coaching insights, and transcripts from other conversations. At the end of the day, I ask Mira to treat the entire thread as context. I specifically instruct it not to give me a simple chronological summary. I want it to identify: the real headline of the day the major themes what shifted what became clearer what remains unresolved patterns in my thinking and behavior places where my actions aligned with my stated values places where they did not blind spots I may be missing strategic feedback potential action items what deserves to be carried into tomorrow I ask for compassion, but I also ask for directness and meaningful pushback. What I Do With the Reflection Once the reflection is complete, I copy it into my journal inside Notion. I also paste the entire reflection into Speechify and listen to it read aloud. Hearing the day reflected back to me is a powerful experience. It allows me to receive the day as a complete experience rather than judging it according to whatever happened most recently. The following morning, I may return to the reflection and carry certain insights into: my meditation my morning pages my planning upcoming coaching conversations my personal development my next committed action What This Practice Has Produced This daily reflection practice has helped me: identify systems producing both desired and undesired results reduce commitments that are not aligned with my priorities recognize recurring patterns in my inner narration protect focused blocks of time more effectively catch distractions more quickly refine my Momentum Methodology strengthen boundaries with my calendar and communication challenge the belief that I am not doing enough recognize how much meaningful work I actually accomplish recover the larger meaning of days that might otherwise feel disappointing There have been many days when I reached the evening feeling as though the day had gone badly. But after receiving the complete reflection, I could see that the narrator's version of the day was incomplete. In more than three months of this practice, there has never been a day when the full reflection supported the conclusion that nothing meaningful had happened. An Important Caution I am not asking AI to tell me who I am. I do not allow AI to make my decisions. I am asking it to reflect a fuller record of my own words and lived experiences back to me. It may offer interpretations, but I remain responsible for evaluating them. I can challenge the interpretation, correct missing context, or reject something that feels untrue or unaligned. The value is not that AI becomes an authority over my life. The value is improved visibility. My narrator will always offer a version of the day. But that version often emphasizes what is unfinished, uncertain, emotionally charged, or potentially threatening. This reflection practice helps me see the whole day. Get My Complete Daily Reflection Prompt I have made my complete daily reflection prompt available to anyone who would like to use or adapt it. Email me at: cliff@cliffravenscraft.com Put the words: Reflection Prompt in the subject line. In the email, tell me one area of your life or business where you would like to create more momentum, and ask me for the prompt. I will personally read your email and send you the complete prompt I currently use for my own daily reflections.

On this episode of Run the Numbers, CJ sits down with Plaid CFO Seun Sodipo. Seun explains how her team is using AI to improve speed, accuracy, and ambition; why finance leaders need both bottom-up experimentation and top-down direction; and how she approaches planning, network effects, and building a durable company.—SPONSORS:RightRev is a revenue recognition platform built for the AI economy, helping finance support usage-based pricing, credits, hybrid contracts, seats plus consumption, and whatever commercial model comes next. It gives product teams the freedom to keep innovating without outdated revenue systems slowing them down. Learn how RightRev can help at https://rightrev.com/cjPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer posts 98% of transactions directly to its ERP, with the remaining 2% routed to a human for review. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered workflows that enforce expense policies at the point of sale, match receipts automatically, and reduce month-end close from weeks to hours. Thousands of companies, including Anthropic, Coinbase, and DoorDash, already run on Brex. Stop asking A-level finance talent to do B-level admin work. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtn—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/seun-sodipo-1498b580/Company: https://plaid.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro2:12 What Plaid does3:53 How Plaid makes money7:04 Eight months in, not new anymore8:57 Sponsors — RightRev | Pulley | Rillet11:48 Career path: Stripe, Glossier, Plaid14:32 Evaluating durability from the outside15:36 Rising expectations and AI17:23 Top-down vs. bottoms-up AI adoption20:06 Sponsors — Maximor | Brex | Anrok23:20 Coolest AI use case: sorting physical mail25:21 How her questions have changed27:11 AI should make you think deeper27:51 Finance AI Day explained31:23 A four-month journey to usable tools32:22 How AI changed her leadership style33:19 Skills that matter more now36:22 The Objective, Strategy, Tactics framework39:56 What planning reveals about an org41:36 Sequential vs. parallel planning45:38 Measuring the value of a network47:33 What people underestimate about Plaid48:56 Lightning round48:56 Screwed up: the reversed page order49:39 Advice to younger self50:32 Finance software stack51:43 Craziest expense: the photo shoots52:20 Credits

A practitioner in Heilongjiang Province has done an in depth examination of his fundamental attachments and determined where he has clung to his desire to arrange things in this life as he sees best, as well as his attachments to self-centeredness. This and other experience-sharing from the Minghui website.Original Articles:1. Getting Rid of My Fundamental Attachment2. The Illusion of My Mother's Illness Was Aimed at My Attachments3. Overcome the Notion of Aging as a Cultivator4. Karmic Retribution for Practitioners Who Take Lives To provide feedback on this podcast, please email us at feedback@minghuiradio.org

Noah Hopton, CEO and Founder of Finvisor, helps startups and growing businesses simplify operations by building integrated back-office teams that combine accounting, finance, payroll, HR, insurance, and technology. By combining experienced financial professionals with modern technology, Noah enables businesses to streamline operations, stay compliant, and focus on sustainable growth.  In this conversation, Noah introduces The Adjacent Extension Framework—Earn the Trust, Build the Relationship, Listen for Other Problems, Connect Other Specialists, and Empower the Team with Tech. He explains why proactive service creates lasting client relationships, how solving adjacent business challenges leads to sustainable growth, and why integrated back-office teams outperform disconnected vendors. Noah also shares how AI is reshaping finance operations by automating repetitive work, empowering finance professionals to focus on strategic decision-making, and helping businesses leverage technology to enhance—not replace—human expertise. — How to Outsource Your Back Office with Noah Hopton  Good day, listeners. Steve Preda here with the Management Blueprint Podcast, and my guest today is Noah Hopton, the CEO and Founder of Finvisor, helping seed and Series A companies that have outgrown spreadsheets and part-time bookkeepers but aren’t ready for a full-time finance team yet. Their job is to give you the financial clarity to make good decisions at every stage of growth. Noah, welcome to the show.  Yeah. Pleasure to be here, Steve.  Well, great to have you here, and I’m very curious about your career and your business and what you built here. I’m particularly curious about your personal ‘Why’ and how you manifest it in your business.  Personal ‘Why.’ That’s great. Well, I’ll be honest, I didn’t go in thinking I was going to be an accountant or run an accounting firm. You know, I studied accounting in school. Eventually, I thought I was going to probably be more in a kind of front-of-house sales relationship because I enjoyed the people part—making relationships and meeting people.  But I was very fortunate that I found the consulting, fractional CFO world, where I got to discover a love of problem-solving, creating relationships, and creating value for clients. For me, it was kind of this love of helping clients understand their business, helping clients understand what to think about around the corner, where it's not just being in-house with one set of books that you're closing.Share on X  When you’re at Finvisor, my day-to-day, at least when I started, was probably working with 10 to 12 clients a month and helping them understand, “Okay, how did they perform last month? Can they hire a certain number of people? And what’s the plan going forward?”  Yeah, I mean, that’s super helpful. I started life in accounting as well with KPMG, and what attracted me was to essentially have that language of business so that I would be able to understand how a business works and have this confidence of not flying blind, right? That’s really, really cool. So how did you evolve from a CFO into a founder? What was the trigger point for you?  So I was very fortunate. I actually was at a prior firm at one point when I started my career, and they were a little bit like the cobbler with bad shoes, where eventually they decided they had to close shop, and clients were going to be given notice. I, myself, was given notice saying, “Hey, in a week, you’re not going to have a job, Noah.” And so I was really given this moment in life, saying, “Hey, if I enjoy what I’ve been doing, do I think I could do it better than the firm I’d been at?  And do I want to make this leap into being a founder and starting a business?” And so my co-founder and I both talked to each other and said, “Look, we love our clients. We love what we’ve been trying to build. I think we just need to do a little bit of a refresh and restructuring of how this operates.” And so we started our own company. I was very lucky that I started with about, I had about 30 clients and a team of four on day one, which I think is unusual.  Most people in the accounting space start off as a one-person shop, trying to grow from one to two, and having to double their clients or double their size to get there. We were fortunate to have five team members and 30 clients on day one. Originally, our vision was just, “Hey, let’s help with the fractional CFO and the bookkeeping,” but that really evolved over time as we added additional services and really understood where our clients were having problems in their back office.  What are the areas where maybe the insurance brokers they’d been working with weren’t very hands-on and kind of came in once a year? Our clients were asking us, as their CFO, “Hey, can you help us select our health insurance?” And we’re like, “Well, we’re kind of doing the broker’s job. Why don’t we build out our own team?” So that was one of the first verticals we moved into and added by building an insurance brokerage. From there, we kept building, where now not only do you have your CFO and accountant helping you, but you also have them with the ability to go out to market, help you compare quotes, and help get your insurance in place. So you’re essentially expanding the array of virtual services that you’re providing, or fractional services that you’re providing, to your clients?  Correct. Yeah. We really try to own the full back office end to end because I think a lot of people deal with, “Okay, great, I have a bookkeeper, I have a tax accountant, I have an R&D tax provider,” and they’re dealing with four or five different vendors that don’t really communicate. The client is the person playing telephone between the two, and we’re like, “Wait, stop. Why is this the solution?” We should just build a different business where it’s all under the Finvisor umbrella.  It’s all full-time team members who are actually working together on behalf of the client, even if fractionally. Some of our clients only need five hours of a payroll specialist, but they need someone to own that role, and they need that person to be able to talk to their sales tax team because it’s like, “Oh, we hired someone in a new state. Is sales tax applicable there?” And connect those dots because, when you have these disconnected providers, you have a lot of things that can drop because they’re not in people’s field of view.  Yeah, I mean, it’s a great service. If you can get a competent team that will take care of your back office, then you can focus on figuring out message-market fit and then essentially scaling revenue. You don’t have to worry about it, and you don’t have to babysit inexperienced people that maybe you can afford to hire, but who would not be able to own the job.  Yeah, exactly. I mean, it’s kind of the, “Do you want to…” You know, I think at least when we started in 2014, there was more of a generalist bookkeeper. That’s kind of the typical solution people went with. Nothing against that, but it’s kind of nice to have dedicated specialists in the different back-office areas that you need. I mean, bookkeepers are great.  They’re usually not your best payroll and HR people. They’re not thinking about California final-paycheck laws, or whether you need to offer a 401(k) if you hire someone in California. Whereas, if you have someone whose entire job is payroll and HR, and you need Finvisor to help run your payroll, they’re going to be thinking about those edge cases and helping you along so that you can just build your business, get to the next milestone, and not worry about tripping yourself up because of compliance, taxes, or a lack of visibility in your reporting. Yeah, that’s great peace of mind. So this podcast is about frameworks, and I wonder, what is your framework? How do you help your clients, or how do you figure things out? What have you developed? We’re about 400 frameworks in, so I’m looking for something unique that helps you and is easy to explain—three to five steps maximum.  Yeah. I mean, one of the ones that comes to mind for us is what we’ve really called the Adjacent Extension Framework. So, first, do really good work and earn your client's trust in one area. Makes it easy for them to approach you.Share on X For us, it’s historically been accounting. People think, “Great, get my books put together.” But for us, it’s really about creating a relationship and earning the client’s trust.  Then, as step two, listen for the other problems they’re having. What are the adjacent problems they’re asking you to solve? And then for us, what we’ve really done is double down in those other areas by building specialists in those verticals. Once you’ve earned the client’s trust, if you’re doing their accounting and all of a sudden they’re struggling with invoicing or collections, you can say, “Hey, we can also help you with accounts receivable and collection efforts because we see your AR balance increasing on your financial statements.” At that point, they’re already thinking, “Great, I like working with this person.  Let’s give their team a try and help us solve another problem.” So, for us, it’s really been about finding those adjacent problems, building a team that specializes in them, and then connecting the client with the right expert. The last piece that’s really coming to market now is using technology to empower the team. Historically, a lot of our value came from having experts who could handle the edge cases or the gray areas between payroll, accounting, taxes, and sales tax. Now, with technology, you can also build the data infrastructure to highlight what’s happening for the client while helping guide the team as they manage those clients.  Love it. So what I’m hearing is, number one—or maybe even number zero—is do a great job, right?  The trust.  Okay. So that’s maybe another way of saying it: earn the trust. But is doing a good job enough to earn that trust, or is there more to it?  I mean, I think in any service business, you want to be proactive. A lot of bookkeepers, accountants, and even legal professionals are usually waiting for the client to ask a question before providing an answer. I think the goal should be to think ahead for the client and proactively provide guidance. That came naturally for us because we sit in the fractional CFO seat.Share on X  But even if you’re just doing bookkeeping, you can still catch these things for clients and help them out. Or if you’re selling P&C insurance and helping clients with their general liability coverage, you can think about what other types of coverage they may need. So I’d say the more proactive you can be, the better. The other thing is meeting clients where they already are.  For us, a lot of our clients are on Slack, so we connect with them on Slack. We chat with them as if we were full-time employees because we don’t want the experience to feel different. We don’t want you to feel like you’re emailing a generic support inbox and not knowing when someone is going to get back to you. If you only need fractional-level support, it shouldn’t feel like you’re getting fractional value or a fractional level of communication.  I love it. So you actually own the function inside the organization, so it feels like you’re part of the team, or your people are part of their team. So that builds the trust. So, do a great job, or earn the trust, number one. Number two, build the relationship. Number three, listen to other problems that they might have. Number four, connect them to other specialists. And number five, empower the team with technology.  Yeah. That’s a lot of it. I mean, as an advisor, we’ve grown… I mean, 60% of our growth comes from client referrals. So I think you know you’re doing something right if clients are recommending you to their friends and network. And so hopefully, if someone’s listening to this and you’re not getting referrals, you should be thinking about, “How do we either create more trust for our clients to be referring us, or how do we become more top of mind when clients are having these conversations?”  That’s great. So 60% of your growth comes from referrals. What’s the other 40%? How do you drive growth? What drives growth for you? What’s the other way to drive growth besides referrals?  Yeah. I mean, I think it’s also being connected with the ecosystem that you’re in. In our space, there are a lot of technology partners. Think about Xero, which is an accounting software, QuickBooks Online, NetSuite, payroll software like Rippling, Bill.com. They all have accounting partnerships, and the more you can build with them and grow your team alongside them, clients will reach out to them and say, “Hey, do you have someone who can help us set up Bill.com or help us set up Rippling?  We don’t have a payroll team to do our state tax registrations.” So we’ve seen a lot of good momentum as our software partners start sending us clients to help us grow. I think the other area is trying to figure out where you can have partnerships that will do introductions. We’ve been very fortunate in partnering with a number of VCs. Obviously, the VCs have worked with us because we’re on the board, or we had a mutual client. A lot of them will start to build partnership channels, and it’s a great opportunity.  They’ll say, “We just invested in this company, and you should go talk to Noah’s team to help with your accounting or your fractional CFO.” So it’s really about finding those tangential operators or entities that complement whatever you’re doing.  So are these primarily personal relationships that need to scale, or do you have a way to scale this across other people in your organization—this ability to develop partners? Or is it mainly you?  It depends on the role. A lot of our fractional CFOs on the team continue to build relationships. I would say probably 40% of our new clients come through a channel that’s not through me. There’ll be other people on our team who have built relationships with another VC or another software company. I think one of the key things we’ve always focused on is hiring people who are very, I would say “doers” might be the wrong word, but people who can self-manage and be project managers.  If you find the right people who can take a step back and look at the bigger picture, I mean, sometimes people come to Finvisor and they don’t realize that we ourselves are a business. Yes, you’re doing accounting like you were in-house and getting the books closed, but if you do good work and you realize clients are having problems, you have to think, “Hey, how can I help clients more and also help Finvisor create a win-win?” A lot of times, when we’re hiring, we’re trying to find people who have that type of drive to continue building and helping us internally, and not just do one part of the puzzle they’re responsible for. That might not be the most direct answer, but I would say a lot of it is hiring—making sure it's not just me leading the growth, but me building a team that can help lead the growth outside of just me.Share on X  Yeah. So how do you share the context so that your team members can connect the dots as well as you can? What’s your approach to that?  There’s a couple of ways we’ve done it. One way is we use a note-taker that then feeds into our CRM. For all client communication, whether they meet with us on Zoom or Google Meet, the transcripts are put into a centralized hub for us. It also connects to our CRM in terms of what we’re doing for the clients. At any point in time, someone can ask, “Hey, what’s going on with this client?” They can understand, “Great, this is what the payroll team talked to them about this week.  This is what the CFO team talked to them about last month. These are the problems they’ve been bringing up.” So we can capture that information without it having to be provided orally every single time, and without having to rely on a chat or an email to the team. There are some moments when it’s useful to give the team a larger update, but in general, it’s good to figure out a way to capture the essence of what you’re doing for your clients so that the team can then, in an AI chat-specific way, talk through, “Hey, great, what’s going on with this client? What are their needs? What has changed in the last six months? Who’s working on the client?”  I’ll have a VC that we’re talking to say, “Oh, we’re looking to invest in the CPG space and this type of vertical. Do you have any clients?” We’re at a point now where I don’t know every client. I usually have an idea about most clients, but there are definitely clients where I don’t know everything that’s happened in the last six months because I don’t talk to all 200 clients.  But I can go to our central hub to gain that information and understand, “Okay, great, which client is looking to fundraise and might want to be connected to this VC?” It’s a nice way to connect the dots. They’re looking to invest. The client is looking to raise. We also do brown-bag sessions. We’re a distributed team, so I think you have to be a little more intentional about how you educate the team. We’ll have weekly meetings where we walk through new technology, new changes in what we’re offering, new positioning, and continue educating the team in a more structured format.  The other thing we’ve done to help the team understand what’s going on is to make information as accessible as possible, similar to how we communicate with clients. So the team doesn’t have to log in to a pretty outdated CRM to pull information on a client. It’s either available directly in the Slack conversation or in a more modern tool like Notion, where you can easily search and find the information you want. So basically, you’re managing and harvesting your data and using that to feed people information about how they can develop partnerships. Is that what I’m hearing?  Yeah. And I think a lot of it is also figuring out which playbooks and processes are repeatable, documenting them better, and then educating the team around them. For example, with our fractional CFOs, we want to be in the board meeting. If we can be in the board meeting, A, we can help clients answer questions about their finances more easily, and B, it’s good to have visibility into what the board is saying about the business and where they want to go.  Then, obviously, the VCs are going to say, “Oh, great, this is Ian at Finvisor.” If he reaches out to me about a partnership, they’re going to have a better understanding of what we do because they’ve been in the room with us—or they’ve been in a virtual or in-person boardroom with us.  So you’re basically sharing the playbook so that they have a better understanding of what they can refer you for. Correct. Yeah.  So, switching gears here, Noah, what’s one thing that you’re trying to actively figure out in your business right now?  I mean, the question everyone is trying to figure out, at least in my space, is how they’re going to use AI in some fashion. That’s the kind of million-dollar question everyone keeps talking about—AI in accounting, AI in finance. Right now, we’re really structured in how we’re trying to use it and apply it. But the question I have is, what’s the next year going to look like? What’s five years going to look like as this technology gets more legs and more trust behind it? We’re pretty intentional about what we’re building and how we’re using some of the newer technology with AI. But I think there’s a lot that, at least for me, you have to continue to iterate. The world today feels different than it did three months ago. I’d say for most of Finvisor’s history—and this has been 12 years—it hasn’t felt like that, where a year later things might feel marginally different because we’re maybe 20% bigger or whatever might have happened.  Now, I think there’s a lot more excitement and unknown around technology and how it can either make people more efficient or help highlight and surface better issues that clients need to talk through. But I also feel like we’re in a moment where everyone’s trying to throw AI into every technology. So we're also trying to stay true to who we are, which is people first, relationships first—technology powering us, not being the solution.Share on X  So as you’re scaling AI to improve the information that your people have, your CFOs have, that presumably is going to lead to people doing less of the mechanical, repeatable tasks and more of the judgment tasks. So how do you scale judgment as you’re scaling the impact with AI?  On our side, I think it’s A, trying to organize and structure the data coming in. B, trying to create tooling that isn’t unique to one client but is built in a way that can be customized for each customer. A lot of the firms I talk to that are in the Finvisor space just take a blanket approach—turn Claude on for every fractional CFO, let them connect it to QuickBooks, and try to figure out their own playbooks.  That’s not how we’ve ever run the business. We don’t just hire accountants and let them run the accounting and see how the output turns out. We’re more focused on figuring out what is actually useful for review. Right now, I think AI has been most helpful around quality. It can definitely check that things are consistent and make sure edge cases are being caught.  I think we’re going to get to a future state where it’s not only making sure quality is at the 95th percentile of confidence, but also giving visibility into metrics like CAC, LTV, and churn—things that would normally take longer to pull together. Your fractional CFO might currently spend hours reviewing Stripe data or Shopify data to come to a conclusion. AI can cut out maybe 40% of that data-cleanup layer, where it’s like, “Okay, now they have the tools to dig in and understand what the underlying problem is,” instead of spending so much time cleaning up the data and getting everything organized.  So currently, at least my thesis is that it’s going to allow us to manage more clients because some of the day-to-day—I don’t want to call it busy work—but the work you have to do before you get to the exciting parts of the job will become more automated and less manual, like pulling data out of Stripe, Shopify, your CRM, or NetSuite.  So does that mean you’ll have a different type of people, maybe higher-level thinkers? Or do you think you can elevate your current team to that level?  Yeah. I think you’re… Sorry, I know I was originally answering this through the fractional CFO lens. Most of our fractional CFOs are already at the top of that organizational pyramid. For them, it’s really about helping them have cleaner data, better visibility into the actions they need to take, and better insight into what they should be reviewing and discussing with the client.  If I think more broadly about the back-office finance team, I do think a lot of the more generalist staff accountant and AP specialist roles won’t be spending as much time on the day-to-day blocking and tackling. If a client has 1,000 transactions a month flowing through their bank and credit cards, historically that accountant would sit in QuickBooks Online clicking “Okay, okay, okay,” reviewing every transaction and coding it. Eighty percent of those transactions will simply be coded automatically in real time as they come in. That leaves them to focus on the 20% that actually requires human judgment.  For me, the question is, can we continue to empower those people to be more impactful with that 20%? Are they the right people for that 20%? We’ve always tried to hire people who are proactive and broader thinkers, so I think we have the right team to step into that. If we’d built a traditional BPO model with an outsourced accounting team made up of people who were really just coding transactions at a basic level, I’d be more worried because getting those people to step up and handle edge cases is difficult.  But that’s not how we’ve historically built Finvisor. We’ve always tried to find people who are a little more… I’d rather hire an A-plus player than a B-player just because there’s some savings in the cost structure. I’d rather have the right people who can perform 80% of the time when they’re at bat than just hire someone because they’re cheaper.  Yeah.  Wrong baseball analogy there, but yeah.  Yeah, I understand. So you have A-plus people. Maybe the people who are doing more bookkeeping-type services—their jobs may become automated—but your A-players are going to have best-in-class information, and they can serve more clients that way.  Yeah. I still think that if you think about the typical accounting structure—if you’re working in-house and you have a bookkeeper and a controller—it’s still helpful. Depending on the size of the company, if you’re a small company, you probably won’t need that bookkeeper.  The controller can handle the edge cases and close the books. But at a certain scale, you’ll still want that junior resource supporting the controller so the controller can focus on the higher-level, more strategic work. I think people will simply be able to do more with less if they’re the right person. There will be people who, if they aren’t good at staying on their toes and figuring out edge cases, won’t be the right fit.  AI will probably replace some of those roles. But I think there’s a great opportunity for people who can think more strategically. They don’t have to be a CFO. They can just be a really smart bookkeeper who’s good at handling edge cases. They’ll simply be able to manage three times as many clients as they could when they had to code every single transaction.  Okay. If you had a magic wand and you could fix one thing in your business over the next 12 months, what would it be?  One area that we probably haven’t prioritized enough because of growth is SEO, AEO, and our overall sales build-out. Our paid advertising hasn’t been the strongest part of our business because it hasn’t been the top priority. If I had a magic wand, I’d have someone clean up our SEO and AEO visibility because I know clients love us and we do great work, but I don’t think we’re showing up the way I’d like from an SEO and AEO perspective. So that would be it. Yeah.  Yeah. Yeah. Love it. So, who are your ideal customers? Who do you want knocking on your door? Is it venture-backed companies primarily, or do you also work with private company founders? Who are your sweet-spot customers?  A lot of our clients are going to be in that 5-to-50-employee range, where they don’t need a full-time back office, a full-time accountant, a full-time CFO, or a full-time payroll specialist, but they need someone to own those roles. That way, we can put together the right Finvisor team to support them. We’ve intentionally made ourselves pretty modular, so while the largest group of our clients is in the tech VC world, we also have a lot of SMBs—law firms, beauty businesses, and other professional services businesses.  I would say that, if you looked at the Finvisor client base as a whole, you’d probably see a lot of startups. But we’re also starting to see more SMBs and more traditional businesses that don’t have VC funding but still need help with their accounting, bookkeeping, and modernizing their back office. So it’s a bit of both. Most of our clients are going to be in that 10-to-50- or 100-employee range, where they’re complex enough that they care about their financials and want to understand what they spent last month, where they’re going, and how they’re going to get there.  Earlier-stage companies are sometimes just a little too early. If you’re a one- or two-person company with just an idea, there’s a reason people think about their financials on more of a cash basis. They can think about the five clients they’re working with. Their bank balance ties pretty closely to their financials. There’s not a huge difference between the two when you’re a sole proprietor.  But as you start to evolve, that’s where Finvisor can provide more value. For all of our clients, we do accrual accounting, so we’re recognizing your revenue and your costs over the life of the service. As you start to grow and build, that’s really helpful. Obviously, if you’re at day one, it’s less impactful because you’re living more day to day, week to week, and month to month.  Steve Preda: Okay. So if we have those kinds of companies—which we do among our listeners—and they hear about this and want to fix their back office and outsource it to a reliable partner who can help them own those functions and give them good advice, what’s the best entry point? Where should they go, and how can they connect with you personally as well?  Yeah. hello@finvisor.com comes to me and the sales team. There’s probably a 95% chance you’ll talk to me if you reach out because I still love connecting with most new businesses that come through the door. The other area I wanted to call out that could be helpful for businesses is PEOs. PEOs are great, but I think at some point clients need to graduate from the PEO, and Finvisor is uniquely positioned to be both your insurance broker—helping you quote large-group plans—and your payroll and HR team to help you leave the PEO.  For a lot of our clients, once they pass that 100-employee mark, it’s like, “Great, we now qualify for a large-group plan,” which might have better rates than what they’re getting through the PEO. They just don’t have the team or bandwidth to get off the PEO. We’ll come alongside those larger companies and say, “Great, let’s quote a large-group plan for you. We’ll also put together a transition plan to register you in the 20 states where your employees are currently located.  We’ll make sure you get your workers’ compensation and employment practices liability insurance in place so there’s really no difference—apples to apples—from being in the PEO to running your own payroll.” We help with that transition because I’m always surprised to see companies with hundreds of employees still on a PEO, where the savings could be in the hundreds of thousands of dollars if they left. They just don’t have the internal team because they’ve always been on a PEO. They’ve never had to do state registrations, so they don’t know how to do them. Because of that, they’re usually not looking for an alternative path to get off that structure. We can at least review it with them and help them out if it’s a good fit. And just to remind our listeners what a PEO is, in case they don’t know.  Oh, sorry. Yeah. A PEO is a Professional Employer Organization. If you’ve heard of companies like TriNet or Justworks, they’re PEOs. In the health insurance space, there are four primary ways you can get health insurance. Most companies start with small-group plans in the early days because they’re state-mandated. For example, in California, if you’re under 100 employees, the rates my company gets would be the same rates Steve’s company gets if we’re both under 100 employees and we’re asking Blue Shield for a quote from the same ZIP code. That’s small-group insurance.  Then there’s level-funded, where carriers quote specifically based on your employee group. There’s large-group, which is somewhat similar but designed for larger organizations. Then there’s the PEO. Let’s say you’re a 10-person company. You don’t have enough employees to qualify for large-group health insurance, which is usually discounted because the risk is spread across hundreds of employees. The PEO says, “We’ll employ your team. Instead of you directly employing 10 people and buying health insurance for only those 10 people, we’ll employ your team and give you rates based on the 10,000 employees we already have.” PEOs are really popular in places like California and New York, where health insurance is very expensive.  But once you get above about 100 employees, you can usually qualify for your own large-group rates, which are similar to what the PEO is getting. The difference is that the PEO is generally marking up those rates because they need to make a margin on the plan. You can often get those rates directly yourself.  Yeah. That makes perfect sense. Okay. So if you’re listening to this and you’re building a venture-backed startup, or you’re the founder of a professional services firm, a law firm, or another small business with 10 to 100 employees, and you don’t yet have the budget—or maybe you simply don’t need—a full-time CFO, insurance advisor, HR leader, and other functional specialists, then reach out to Noah and Finvisor.  Check out what they have to offer and see what services might be a good fit for your business. Thanks, Noah, for coming on the show and sharing your expertise. It’s fascinating to see how this field is evolving, how you’re tapping into technology, and how you’re focusing on the highest-quality CFOs to help your clients. If you enjoyed this conversation, stay tuned.  Follow us on YouTube, Apple Podcasts, or wherever you get your podcasts. Make sure you don’t miss an episode. Every week, we bring you exciting entrepreneurs and their best management frameworks. Thanks for coming, Noah, and thanks for listening.  Thanks, Steve. Appreciate it. Important Links: Noah's LinkedIn Noah's  website Noah's email: hello@finvisor.com

How can architecture leaders navigate economic uncertainty, build authentic brand voices, and transform practice operations through live connection and practical AI tools?In the Season 13 premiere of Practice Disrupted, host Evelyn Lee opens the season with a dynamic roundtable featuring Aya Schlachter and Nikita Morrell. Aya is the founder and CEO of MGS Global Group, the production partner behind more than 500 architecture firms worldwide, and host of the AI for Architects and Architect My Business podcasts. Nikita is an architecture messaging strategist and the founder of Dirt, a brand messaging agency for AEC tech and construction companies. Together, they discuss the state of the industry, why traditional practice models need a fresh approach, and why they co-founded Archicon, the "unboring business conference" landing in Melbourne, Australia.The discussion explores the pressing business challenges facing AEC firms today, from drying pipelines and economic uncertainty to standing out in a crowded market. Rather than relying on traditional formats, Aya and Nikita break down how Archicon flips the standard conference setup by using shorter 15-minute presentations, extended Q&A sessions, candid panels on firm failures and regrets, and a total ban on all-black dress codes. The trio also delves into the operational shifts necessary to build sustainable practices - emphasizing how clear systems, business model refinement, and operational efficiency give leaders the headspace needed to think strategically about their firm's future."You can be professional and have personality, and you can learn and have fun too. It doesn't have to be so serious." - Nikita MorrellLooking toward the future of practice, Evelyn, Aya, and Nikita tackle content creation, personal branding, and the practical implementation of AI. From leveraging dynamic tools like Notion and custom AI agents to guardrail email workflows and record repeatable tasks, they emphasize that technology should support human judgment rather than replace a unique brand voice. They also preview an upcoming masterclass collaboration between Evelyn and Nikita designed to give practitioners actionable, high-impact AI skills for positioning and practice growth.Guests:Nikita Morell is a messaging strategist who has spent over a decade helping architecture firms around the world articulate their value and win better projects. She is the co-founder of Archicon and founder of DIRT, and is widely known for her sharp, thought-provoking insights on branding and business in the AEC industry.Aya Garcia Shlachter is the Founder and CEO of MGS Global Group and co-founder of Archicon, with nearly two decades of experience helping architecture firms grow and scale. A recognized entrepreneur, AI thought leader, and podcast host, she empowers architects to build future-ready practices through strategy, technology, and leadership.This episode is especially for you if:✅ You want to explore new, "unboring" formats for business development, industry networking, and conferences like Archicon.✅ You are looking to overcome pipeline slowdowns and differentiate your practice through clear positioning and distinct brand messaging.✅ You want to streamline practice operations using systems like Notion, ClickUp, and CRMs to reduce mental load and gain strategic headspace.✅ You want to build authentic trust with potential clients by embracing content creation, personal storytelling, and human-centered marketing.✅ You are seeking practical, actionable ways to integrate AI, speech-to-text tools, and custom workflows into your firm without losing your unique practice voice.What have you done to take action lately? Share your reflections with us on social and join the conversation.

This episode is a re-air of one of our most popular conversations, featuring insights worth revisiting. This week on The Data Stack Show, Eric Dodds and John Wessel explore how AI is reshaping the data industry, focusing on the ongoing cycles of bundling and unbundling within data infrastructure. They discuss the potential for closed ecosystems like Notion to deliver personalized, integrated experiences and examine recent industry moves such as Fivetran's acquisitions. The conversation also highlights the challenges faced by both startups and incumbents, the influence of enterprise customers on product development, and the enduring importance of trade-offs when choosing between bundled and unbundled solutions. Key takeaways include the complexity of implementing AI across platforms, the likelihood that market cycles will persist despite technological advances, and the need for organizations to carefully weigh integration, flexibility, and long-term risk when adopting new data tools. Highlights from this week's conversation include: AI's Value and Early Ecosystem Integration (1:11) Closed Ecosystems and AI Opportunities (3:21) Personalized Software and the Blank Page Problem (6:17) Transition to Data Industry: Bundling Trends (9:56) Market Cycles and AI's Role in Bundling (12:56) Incumbents, Innovation, and AI Layering (15:53 Longevity of Legacy Systems and Ecosystem Risks (17:56) Switching Costs and Incumbent Advantages (20:33) People Dynamics and the Startup-to-Incumbent Arc (22:50) Enterprise Data Infrastructure: Engineering Challenges (26:33) Fragmentation, Bundling Value, and AI's Insulation Effect (29:54) Too Many Tools: The Real Meaning Behind Bundling Demand (31:36) Trade-offs in Bundling, Unbundling, and AI (33:40) Final Thoughts and Takeaways (34:34) The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. Each week, we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data. RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ever wanted to snoop behind the scenes of a seven-figure business and see exactly what tools keep the whole thing running? Same. Brandi is nosy like that too.In this episode, she's pulling back the curtain on her full 2026 tech stack, every piece of software she uses to run Serve Scale Soar and all seven of its revenue streams. But here's the honest part: you do not need all of it.Brandi walks through what she recommends for service providers, what she'd do differently if she could go back, and the three tools she's actively cutting as AI keeps getting better. Grab your coffee and let's do the dang thing.IN THIS EPISODE, YOU'LL LEARN:The only two all-in-one tools a service provider actually needs (and when to add more)Why Brandi runs 90% of her business inside FG Funnels, and who should skip itHow Claude built a full Notion revenue dashboard for her in 15 minutesWhy she still recommends Asana over ClickUp and Notion for most freelancersWhich AI tools she pays for (only 2) and which she uses on the free planThe 3 tools she's cutting in 2026 and the real reason whyWhy simplifying your stack matters more than the software bill itselfMENTIONED IN THIS EPISODE:HoneyBook (30% off first year): brandimowles.com/honeybookHoneyBook in a Hurry ($37 shortcut with one-click installs)FG Funnels (discount codes): brandimowles.com/fgfunnelsConversions for Clients (early-stage service providers): conversionsforclients.comThe Strategist Society (scaling past $10K months): thestrategistsociety.comREADY TO SCALE PAST $10K MONTHS?If you're a one-on-one service provider ready to build big-girl-money months with real support, come see what we're building inside the Strategist Society. It's the room, the community, and the daily voice support that helps you scale without hustling yourself into the ground. Head to thestrategistsociety.com.LOVED THIS EPISODE?Screenshot it, share it to your stories, and tag @brandimowles so Brandi can hang out with you over there. Now go do the dang thing.Follow the Podcast: https://podcasts.apple.com/us/podcast/serve-scale-soar/id1477998650Follow Brandi on Instagram: https://www.instagram.com/brandimowlesFollow Brandi on Facebook: https://www.facebook.com/Brandiandcompany

Just one week off our big 2.0 launch, Shaw and Chris hop on to talk about how email is actually a big part of the launch. Social media ain't what it used to be, and not everybody is on your app every single day waiting for a notification. Sponsor: Notion With the recent launch of Custom Agents, Notion became the collaborative AI workspace where teams and agents work side by side. And now, their new Developer Platform is turning that workspace into infrastructure developers can build on. Time Jumps

Show DescriptionCodePen 2.0 is out now and we're talking about the launch, the idea of templating on the web, and how HTML could look very interesting in the future. Listen on WebsiteWatch on YouTubeLinks Yarn Announcing TypeScript 7.0 CodePen 2.0 announcement blog post SponsorsNotionWrite custom tools for Notion Agents that generate assets, query live data, and hit any API. Listen for incoming webhooks from any app, then run workflows with Notion Agents, pages, databases, and external APIs. All of this, on a hosted runtime. Workers are isolated sandboxes managed by Notion, so the code behind your syncs, tools, and workflows runs on our infra instead of your servers.

A few weeks ago, in Episode 827, I talked about attention, distraction, and intentionally subtracting the things that pull me away from what matters most. But removing external distractions doesn't solve everything. I've discovered that some of my strongest distractions come from inside my own mind. A new idea appears. I remember something I need to do. I suddenly want to build something in Notion. And before I know it, I've abandoned the very thing I intentionally blocked time to accomplish. In this episode, I share two simple practices that have dramatically changed how I protect my focused work: Capture and Return When an important idea or reminder comes to mind, I capture it quickly without acting on it. This matters—but not now. I don't have to dishonor a good idea in order to honor my current commitment. Release and Return Sometimes I realize the thought doesn't deserve any further attention at all. Or sometimes I discover that I've already wandered down a rabbit trail. Instead of judging myself or turning the distraction into failure: No shame. No drama. Notice it. Release it. Return. That led me to a deeper realization: The goal is not to never get distracted. The practice is learning to return. I also explore how this connects with more than 1,600 consecutive days of meditation and one of the central statements from my personal manifesto: Discipline is the loving protection of what I am devoted to. Protected focus starts with knowing your desired outcome, understanding why it matters, identifying the next aligned action, and putting that action on your calendar. Then, when the time comes: Protect what you said you were devoted to. Capture and return. Release and return. And keep returning. Ready to Create More Momentum? If you have a desired outcome but need greater clarity, honest reflection, strategic feedback, aligned next actions, calendared commitment, protected focus, and relational accountability to help you move forward, I invite you to explore Next Level Mastermind: Momentum. Learn more at: https://cliffravenscraft.com/momentum

On this special episode of Run the Numbers, CJ Gustafson revisits standout moments from past conversations with Alex Immerman, Curt Sigfstead, Daniel Kang, Adam Ante, and David Laptor. Together, their advice reveals how great CFOs earn trust, challenge founders, speak up when the data says something is wrong, and become true strategic partners without relying on the power of the purse strings.—SPONSORS:Anrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer cut their close in half, took audit findings from seven to zero, and cut back-office costs by 70% in six months. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro3:27 Alex Immerman's favorite CFO interview question4:54 What a weak answer looks like5:26 When the hard calls actually matter6:37 Walt and Roy Disney7:36 Liked even when making unpopular calls8:43 Sponsors — Anrok | RightRev | Pulley11:35 Curt from Clio: ego in the bottom drawer13:52 CFO as supporting cast15:10 Backbone vs. ego: when to use each16:23 Daniel Kang: don't use the purse strings as power17:36 Earn the seat, don't demand it18:52 Money is one arrow, not the whole quiver20:39 Sponsors — Rillet | Maximor | Brex23:54 Adam Ante: finance people pigeonhole themselves26:46 The CFO can sit in any meeting27:30 First 90 days: listen before suggesting28:46 David Laptor: listen, observe, think, speak30:50 Build a culture where speaking up is welcomed31:51 Curt from Clio: don't come in as a know-it-all33:08 Earn trust through curiosity33:46 Beware of all-green dashboards35:28 What would CJ say to Ballmer?37:14 Parting thoughts from Ben38:00 Credits

Episode Summary Michelle opens with the question a lot of solo PR pros have been quietly asking themselves: if I call myself a communications generalist on my website right now, am I costing myself money? Karen's answer is immediate — not 'am I?' but 'you already are.' What follows is a data-driven, practically grounded conversation about the specialization economy: the growing body of research across the freelance and independent consulting world in 2026 that shows generalists getting squeezed and specialists pulling away. Karen and Michelle aren't just reporting a trend — they're translating it specifically for PR and communications practitioners who've never had anyone apply this research to their work. The episode covers the bimodal income distribution hiding inside freelance averages, the vertical-horizontal framework for finding your niche, four common objections to specializing (with honest answers to each), a three-question filter for identifying your niche, and the metric-capturing habit that makes specialization pay off over time. This is a conversation for the solo practitioner who has 'I do everything' on their website tonight — and might be ready to change it. Episode Highlights [00:03] The Opening Question That Frames Everything: Michelle opens before the intro music with a direct question to Karen: if she calls herself a communications generalist on her website right now, is she costing herself money? Karen's answer: not 'am I?' but 'you already are.' The episode's premise is immediate and personal — and Karen and Michelle make clear they're talking to themselves too. [01:05] The Specialization Economy: What the Data Shows: Freelance and independent consulting data in 2026 is pointing in the same direction across multiple sources: generalists are getting squeezed and specialists are pulling away. The average US freelancer earning rate hides what Karen calls 'a canyon' — generalist content and writing on the low end, specialists in high-demand niches billing well over $100 an hour on the high end. Almost nobody is actually earning the average. The floor is dropping for generalized skills; the ceiling is rising for specialized ones. Karen's framing: the middle — 'I'm pretty good at a lot of things' — is where people get stuck. Note: some figures referenced in this episode are still working through the show's verification process; sourcing details will be linked in the resources section as they are confirmed. [04:13] Why Specialization Wins: The Practical Case, Not the Philosophical One: The argument for specializing isn't philosophical — it's structural. A generalist PR consultant competes with an enormous pool of other generalist PR consultants. Someone who specifically handles crisis communications for mid-size healthcare systems competes with a much smaller, more identifiable group. Smaller pool, higher rates, and — critically — the client doesn't have to explain their industry from scratch. That last point is underrated: starting a client engagement already fluent in their world, their vocabulary, and their stakeholders is worth real money. Karen also flags a related shift: companies are increasingly requiring proof of impact before hiring specialists, not just portfolios. That proof is much easier to produce when you've done the same kind of work for the same kind of client repeatedly. [06:40] The Vertical-Horizontal Framework: What Niching Actually Means in Practice: Karen and Michelle push back on the idea that niching just means picking an industry. The framework showing up across freelance research: pick a vertical (the industry — healthcare, legal, fintech, sustainability, professional services) and a horizontal (the service — media relations, crisis management, thought leadership, internal comms, funding round communications). Your niche is the intersection. Examples drawn from recent guests: Sharon Toerek does IP and marketing law for independent agencies. Kara Ryan came up through healthcare communications and built an advisor-led, AI-powered practice on top of that. Both dialed in the vertical and the horizontal. The practical test: once you say your niche out loud, it should stop sounding like a limitation and start sounding like a positioning statement. [09:06] The Filtering Benefit Nobody Talks About Enough: When you're specific, the wrong-fit inquiries mostly stop coming in. You stop getting the 'can you also just quickly help with our internal newsletter' request from an industry you don't want to be in. Positioning does some of your qualifying for you before the discovery call even happens. Karen and Michelle note this is deeply connected to scope creep — a topic worth its own episode. [10:02] Specialization Is an Income Stability Conversation, Not Just a Rate Conversation: Once you're known for a specific thing, you stop pitching one-off projects and start getting asked to stay. Broader freelance data shows a large majority of hiring managers plan to lean more on freelance and fractional talent for ongoing work — and that shift toward retainers happens specifically because specialists make ongoing relationships easy to justify. Nobody keeps a generalist on retainer. The FinTech thought leadership expert stays. The practical consequence: niching is often what makes the retainer conversation possible in the first place, and retainers solve the feast-or-famine cycle that most solo practitioners experience. [12:41] Four Objections — With Honest Answers: Karen and Michelle work through the four most common pushbacks they hear in the Solo PR Pro community. One: I'll turn away good work and go broke. Honest answer — there is a real ramp-up period of roughly six to twelve months; don't torch your existing client base overnight, shift new business conversations while honoring existing relationships. Two: my market is too small. Counter with math — a few hundred mid-sized healthcare systems in the country, you only need a handful of retained clients for a full solo practice; smaller pool of competitors is not the same as a smaller pool of clients. Three: I'll get bored. Flips the other way — as a generalist, every new client is a cold start; as a specialist, the energy goes into strategy instead of orientation. Four (the quiet one): what if I pick the wrong niche?  [16:16] The Three-Question Filter (Plus an Unofficial Fourth): A practical framework for identifying your niche. Question 1: Where do you already have an unfair advantage? Past industry experience, a network, credentials, lived experience — something that means you start ahead of a stranger walking in cold. Question 2: Where's the budget? You can be brilliant in a niche that simply doesn't spend on PR. Healthcare, legal, financial services, B2B tech consistently show up as categories with real comms budgets. Question 3: Can you say it in one sentence — and does that sentence make a stranger say 'I know exactly who needs you'? If it requires three qualifying clauses, it's not sharp enough yet. The unofficial fourth: are you willing to hold the line publicly? Your website, your LinkedIn, your pitch all need to stay consistent or the positioning won't do its work. [18:11] Running the Filter Live: The In-House Bank Example: Karen and Michelle run a hypothetical listener through the filter: six years in-house at a regional bank, now doing a bit of everything for small business clients. Unfair advantage: already fluent in financial services vocabulary, compliance, and regulatory relationships — most PR consultants would need a year to learn that. Budget: financial services and fintech are consistently well-funded for communications. One sentence: 'I help community banks and credit unions navigate media and regulatory communications.' Karen: say that at a conference and watch how fast someone says 'I know someone who needs that.' [20:28] How to Prove It's Working: The Metric-Capturing Habit: For every engagement going forward in your niche, capture one number — one sentence, one metric, one outcome. Not a dramatic case study, just: 'Positioned the founder as a category expert; three inbound press inquiries within a month of the first byline running.' Build a running document, whether in Notion, a notes app, or wherever your system lives. Michelle: build it into your closeout process for every wrapped engagement, and into every campaign, not just every full client relationship. Karen: future you will be very grateful. Related Episodes That Solo Life, Episode 343: Sharon Toerek on Legal Protection, IP, and Building a Specialized Practice That Solo Life, Episode 341: Kara Ryan on Going Solo After 20 Years in Healthcare Comms Resources & Additional Information Doers Circle: The Future of Freelancing in 2026: 8 Trends Solopreneurs Can't Ignore Venture Lab: 10 Freelancing Trends in 2026 (Rates, Niches, and Client Expectations) Solo PR Pro membership community: soloprpro.com That Solo Life podcast website: thatsololife.com Host & Show Info That Solo Life is a podcast created for public relations, communication, and marketing professionals who work as independent and small practitioners. Hosted by Karen Swim, APR, President of Solo PR Pro, and Michelle Kane, Principal of Voice Matters, the show delivers expert insights, encouragement, and practical advice for solo PR pros navigating today's dynamic professional landscape. Listen to all episodes and catch up on previous conversations at thatsololife.com. Did this episode inspire you? If you found value in this conversation, please take a moment to leave us a review on your favorite podcast platform. Your feedback helps us reach more solo pros just like you! Don't forget to subscribe so you never miss an episode.

Oliver drops some of his latest work, including his new collaboration with Kryder & The Young Punx “AEIOU”, and his HI-LO collab with Vini Vici “Pump It Up”, alongside new music from Cloonee & Prospa, Tiësto & Caleb Arredondo, NOTION & X CLUB, Lost Frequencies, Dom Dolla, Curbi, GAWP & Aden Rémai and many more on #HeldeepRadio!

This week VisionV picks his Up All Night tracks & Barakuda is on Guest Mix duties.1. Claude VonStroke & Rebūke - I'm Just Calling 00:00:432. John Grand - MoMa 00:05:453. Nautik - Get Busy 00:11:034. Foster The People - Pumped Up Kicks (OMRI. Remix) 00:14:535. Wildchild - Bring It Down (Ridney Remix) 00:20:236. Karen Harding - Island 00:24:517. Layton Giordani & KASIA - The Realm 00:28:478. Kaufmann & Oliver Huntemann - K.O. 00:30:479. Julian Jordan - Bad Bitch 00:34:5810. Michael Grandel - Eta Carinae 00:38:0311. Ramon Bedoya & TheConnect - Cigarette 00:43:1012. BROSA - Skip Don't Play It 00:46:2813. Liam Denver ft. Alita Moses - I Don't Wanna Be Right 00:49:2314. Loco Dice - Hold Up (You Feel That) 00:52:1215. MESSIE & Juni ft. sbk - B2B 00:54:5216. NOTION & X CLUB.- U KNOW 00:58:0817. Eli Brown - Electrify 01:02:3818. HILLS - Lift Me Up 01:06:3619. Voltage - Music Is The Answer 01:09:3720. Logistics - Chant (Lens Unglued Remix) 01:13:3521. Pocket & Oppidan - ur world 01:18:1622. VisionV & Jex - Fate 01:21:3523. Jay Robinson & Axwell - Free Again (Axwell Cut) 01:26:0224. Barakuda - Guest Mix 01:29:37

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:ChatGPT Health Syncs Apple and Medical DataClaude Voice Mode Adds Opus and SonnetClaude Voice Mode Supports ConnectorsMicrosoft MAI Image 2.5 Pro Launch DetailsMicrosoft MAI Image Model Benchmark PreviewGoogle Gemini 3.6 Flash and Flashlight ReleaseGemini 3.6 Flash: Token Efficiency UpgradesGoogle Gemini Spark Agent for Task AutomationClaude Cowork "Record a Skill" With Voice NarrationChatGPT Voice on Desktop: Full Jarvis ModeChatGPT Voice Controls Apps via App ShotsCross-Platform AI Skills Sharing (Claude, Codex, GPT)Timestamps:00:00 Recent AI feature updates05:22 Unified health data management09:52 New voice feature explanation11:28 Launch of Microsoft's new image model16:17 Explaining the Gemini 3.5 models17:11 Developers benefiting from 3.6 Flash22:45 Introducing Gemini personal intelligence25:10 Claude Cowork's new skill feature28:32 New default feature in Claude Cowork34:22 Using AI like Iron Man35:09 Excitement for future AI advancements38:20 Wrapping up and subscribingKeywords: ChatGPT Jarvis mode, ChatGPT Health, OpenAI, Anthropic, Claude voice mode, Claude Cowork, Claude record a skill, Microsoft, MAI image 2.5 Pro, AI image generator, Google Gemini, Gemini 3.6 Flash, Gemini 3.5 Flashlight, Gemini Spark, Google AI agent, AI-powered personal assistant, AI agents, Agentic workflows, Multimodal AI, Token efficiency, Image generation, Voice-activated AI, AI-powered task automation, App shots, GPT Live, Remote browser, Computer code execution, Slack integration, GitHub integration, Notion, PowerPoint AI features, Workspace plans, Apple Health integration, Medical records AI, Health data privacy, Consumer AI, Chronic condition management, AI-powered document processing, AI for business, AI model benchmarking, AI for developers, AI economics, Personal intelligence, Automated triggers, Google Docs AI, Team collaboration AISend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Felix Rieseberg leads engineering for Claude Cowork and Claude Code Desktop at Anthropic. Before that he led desktop and web infrastructure at Notion and built software at Stripe, Slack, and Microsoft. In this episode of Summation, Felix and Auren discuss:The next AI step function: going from solving a problem to owning a responsibilityWhy AI will win many Nobel prizes before it wins a single PulitzerBringing AI into the physical world, from a $30 coffee-machine display to a Wi-Fi garage openerWhy "here's what your team can learn from the Navy SEALs" is bad management adviceYou can find Auren Hoffman on X at @auren and Felix Rieseberg on X at @felixrieseberg