AI for Founders with Ryan Estes

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AI for Founders is where 47,000+ founders learn to build and scale with AI. Hosted by Ryan Estes, a Denver investor, creator, and founder, the show breaks down real strategies from top operators and AI visionaries. AI-ready data, zero-dependency workflows, founder-led distribution, and the tools driving revenue for today’s fastest-growing companies. If you’re a technical or non-technical founder who wants to work smarter, scale faster, and stay competitive, this podcast is your weekly unfair advantage.

  1. Peptides, and the Future of Human Performance

    3 UUR GELEDEN

    Peptides, and the Future of Human Performance

    Dr. Ian Ellis, Founder of voafit.com | Precision Dosing, Peptides, and the Future of Human Performance The story starts in an emergency room. Dr. Ian Ellis spent almost a decade watching the same patients cycle through with the same problems, receiving the same treatments, never actually getting better. Just bailing out the pool, he says. That disillusionment became the seed of something bigger. When Dr. Ellis was prescribed semaglutide in 2022, he experienced what millions of people experience every day: real weight loss with a catastrophic tradeoff. Thirty pounds gone. But the body composition scan told the real story. He had lost twice as much lean mass as fat. Same body fat percentage. Just a lighter, weaker version of himself. The drug had done what it was designed to do. The problem was the dosing. That realization sent him down a research rabbit hole that ended with the founding of his concierge practice and an app called My Level, designed to help patients find their minimum effective dose of GLP-1 medications, not the maximum tolerated dose. The results at his clinic have been remarkable: faster weight loss than clinical trials, on half the medicine, with zero desistance due to side effects. Key Frameworks: The Precision Dosing Model: Standard GLP-1 protocols escalate doses on a fixed schedule regardless of individual response. Dr. Ellis argues this is backwards. The goal is to find the lowest dose that suppresses appetite just enough to create a 500-750 calorie deficit, not to eliminate hunger entirely. Think dimmer switch, not on/off toggle.The Appetite-as-Physiology Framework: Willpower is not a weight loss strategy. Dr. Ellis compares appetite suppression to sleep deprivation. You can fight it for a day or two, but biological drives increase in intensity until they become inevitable. The solution is not discipline. It is solving the physiologic problem.The Nutrition Hierarchy on GLP-1s: Because appetite is suppressed, what you eat first matters enormously. If you fill up on carbs, you will never reach protein and plants.The Cost-Convenience-Quality Triangle: You only get two. Cheap and convenient equals low quality. Convenient and high quality equals expensive. Inexpensive and high quality means you are cooking it yourself. There is no fourth option.Peptides as Information Systems: Peptides are strings of amino acids that act as keys for specific biological locks. Their safety profile is relatively predictable because they bind to one receptor and produce effects that follow logically from what that receptor does. GLP-1 receptor? Suppresses appetite and slows gastric motility. Overdose? Stomach stops working. Predictable. Manageable.The 15% Body Fat Sweet Spot: Evolutionary biology has calibrated human attraction toward function, not aesthetics. Studies show 15% body fat consistently ranks as most attractive across populations because it signals strength, capability, and survivability. Single-digit body fat is not optimal health. It is a performance liability.https://voafit.com/ https://www.linkedin.com/in/voafitmd/ ⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠ ⁠⁠https://aiforfounders.co⁠⁠ ⁠⁠⁠https://kitcaster.com/application ⁠⁠⁠ ⁠⁠⁠https://ryanestes.info⁠⁠⁠

    58 min.
  2. IRL Events Are the New Moat

    23 UUR GELEDEN

    IRL Events Are the New Moat

    Show Notes ★★★★★ You can automate your outreach. You can spin up agents overnight. But you cannot automate the moment someone walks into a room and feels seen. Virginia Fishcorn has produced several hundred million dollars worth of live events across 18 years. She is the founder of Party Trick, a platform she describes as having a professional event planner in your pocket. Her user is not the professional event planner. It is what she calls the Secret Event Planner: the founder, the team lead, the parent, the person who got voluntold into hosting something they've never done before and needs to not embarrass themselves. This conversation is a masterclass in why the further we go into technology, the more a well-designed room is worth. Frameworks from the Episode Start with the Why Virginia returns to this principle every time the conversation drifts toward logistics. Before you book the venue, before you curate the guest list, before you order the swag, ask why you are doing this event. Is the goal press? Lead gen? Community? A brand moment? A splashy launch? The answer to that question changes every single downstream decision. Founders who skip this step run events that feel busy but accomplish nothing. The Secret Event Planner Party Trick was not built for professional event planners. It was built for the person who is suddenly responsible for a networking happy hour or a product launch and has never done it before. Virginia calls this person the Secret Event Planner. The platform walks them through blueprints, timelines, and checklists so that the basics are covered and the founder can spend mental energy on the things that actually create memory. Engineer the Room, Do Not Just Fill It Guest list curation is a strategic act. Virginia deliberately mixes people across career stage, industry, and background because friction between unlike people creates energy. She also recommends going 60/40 for community-building events: 60% recurring attendees to create the sense of tribe, and 40% new faces to keep it from going stale. A room full of people who are exactly alike is comfortable and forgettable. The Peaks, Pits, and Bookends Principle (The Power of Moments) People do not remember the middle of an experience. They remember the beginning, the end, and the moments that surprised them. Virginia designs for surprise and delight deliberately: a magic eight ball at a trade show booth, a garden gnome hidden in the bathroom, a key party fishbowl at a product demo. These are not gimmicks. They are engineered memory anchors. Start Small and Get the Reps Virginia told Ryan the same thing she told her 11-year-old son before a difficult apology: practice in safe spaces before you do the big thing. A 10-person dinner in your living room is a real event. It gives you the reps to become a confident host. Confidence is not cosmetic. Guests read the energy of the host immediately. If the host is anxious, the room is anxious. The Duck Principle Something will go wrong at every live event. The job of the host is not to prevent this. The job is to respond with the energy of a duck, calm on the surface while paddling underneath. No one in the room knows what was supposed to happen except you. If you act like it was planned, most people will believe you. Pre, During, and Post: The Full Arc of an Event Virginia sends playlists after her parties. She makes introductions via email after the night ends. She helps clients craft follow-up moments that extend the experience and deepen the memory. The event is not over when the last guest leaves. That post-event window is one of the most underused tools founders have for building real relationships. https://partytrick.com/ https://www.linkedin.com/in/virginiatfrischkorn/ ⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠ ⁠⁠https://aiforfounders.co⁠⁠ ⁠⁠⁠https://kitcaster.com/application ⁠⁠⁠ ⁠⁠⁠https://ryanestes.info⁠⁠⁠

    1 u 3 m
  3. The Behavioral Health Crisis Is a Data Problem

    3 DGN GELEDEN

    The Behavioral Health Crisis Is a Data Problem

    The average patient gets seen and disappears. No signal, no follow-up, no data trail. Just a receipt and a co-pay. Lauren Larson, CEO of Videra Health, knows exactly what lives in that gap, and he has spent six years building AI to close it.Lauren came up through HireVue, where video-based AI interviewed 100 million job candidates and surfaced the best ones through behavioral signal, not resume gatekeeping. When the company sold in 2019, he took everything he learned about reading humans through a screen and pointed it directly at behavioral healthcare.The Problem Videra Is SolvingThe system rewards patients who are good at making appointments. The people who are actually in crisis, the ones who missed their last visit, the ones who stopped their medication because of nausea, the ones who are not sleeping, those people spiral quietly. Videra uses AI-powered check-ins via audio and video to reach those patients between appointments, collect behavioral data, and surface the ones who need intervention back to their clinical teams.The platform is not trying to replace providers. It is trying to make sure providers only get interrupted when it actually matters.Core Frameworks DiscussedPassive vs. Structured Assessment: Lauren emphasizes the difference between conversational AI that just listens and structured clinical AI that knows which questions to ask first. The opening prompt is everything. Random check-ins produce noisy data. Calibrated sequences produce signal.Observational Biomarkers at Scale: Rather than guessing which features predict a condition, Videra trains on as many features as possible and lets the model surface what matters. The goal is 30 to 40 observational biomarkers detected in a single two-minute session, tracking movement, voice, language, and facial affect over time.The ROI Problem in Healthcare Innovation: Cool technology does not get deployed unless someone can pay for it. Lauren learned this lesson early. Videra had to expand beyond assessment into clinical documentation, patient intake, and provider coaching before the sales motion started working.Bias Testing Through Model Cards: For every predictive model, Videra builds model cards that track false positive and false negative rates across demographic and intersectional groups. Not just men vs. women. Not just race. But black women vs. black men vs. white women, and so on. Then they monitor for drift over time.The Elevate Product: AI that listens to provider-patient conversations and gives clinicians direct, specific feedback on where their empathy broke down and what they could have done differently. The goal is not to replace human care. It is to make every clinician perform closer to their best.Founder Experiment: Build a Behavioral Signal Intake BotUsing a voice or text-based AI agent (Claude, GPT-4o, or a similar LLM with tool access), build a simple structured intake flow for your product that collects behavioral signal, not just preference data.Start with three seed questions designed to elicit emotional state rather than factual answers. Log the responses. After 10 interactions, review the transcripts and flag any response patterns that correlate with disengagement, churn risk, or user distress. Run that as a lightweight customer health model before you ever touch a clinical dataset.If your product drives human decision-making in any way, behavior is your biggest data layer. This experiment will show you how much you are currently leaving on the table.⁠https://viderahealth.com/⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠⁠⁠⁠⁠https://aiforfounders.co⁠⁠⁠⁠⁠⁠⁠https://kitcaster.com/application⁠ ⁠⁠⁠⁠⁠⁠⁠https://ryanestes.info⁠⁠⁠⁠

    50 min.
  4. The AI Analyst That Never Sleeps: Burak Karakan of Bruin

    3 APR

    The AI Analyst That Never Sleeps: Burak Karakan of Bruin

    Say Hello to Your AI Data Analyst: How Bruin Is Replacing Headcount, Not Just Dashboards There is a question Burak Karakan wants every founder to ask themselves right now: Do you know where your agents are? Burak is the co-founder of Bruin, an AI data analyst that connects directly to your data warehouse and answers any question in under 90 seconds, right inside Slack, Microsoft Teams, or a clean web UI. No dashboards to wrangle. No tickets to the data team. No waiting two days for a report that is already out of date by the time it lands in your inbox. Calling in from Istanbul, one of the world's oldest crossroads of culture and commerce, Burak brings the kind of perspective that only comes from years of building data infrastructure inside big enterprises and scrappy startups alike. That experience is the foundation Bruin was built on. The Framework: Data as the Operating System of Agentic AI Burak lays out what he calls the Virtual Data Team model. As companies begin spinning up multiple AI agents across marketing, sales, operations, and support, those agents will need to collaborate, just like humans do. The missing piece is not more agents. It is a centralized, governed, trustworthy data layer that all of them can query reliably. Bruin fills that role. Think of it as the data team member that every agent in your org chart can ping before making a decision. Key principles of the framework: Data still lives in your warehouse (Snowflake, BigQuery, etc.). Bruin does not move or copy it.Every action the agent takes is traceable. You can click through and see exactly how it arrived at any answer.Granular access control means marketing agents only see marketing data, while executive channels get broader access.Multiple deployment models are available: fully managed cloud, hybrid, or fully on-premise with your own LLMs.An MCP server exposes Bruin's full capabilities so other agents can query it programmatically.The Experiment: From Reactive to Proactive Intelligence Burak draws a sharp distinction between a reporting tool and a reasoning system. Bruin starts as the former and is actively evolving into the latter. Current customers are already using it to route incoming support tickets through the AI analyst before the support agent even sees them, pulling customer purchase history, validating claims, and generating accurate responses. What used to take two to three hours per ticket now takes about 40 seconds. The next frontier Burak is building toward: agents that proactively surface problems you have not thought to ask about yet. Upcoming capacity shortfalls. Campaign spend misaligned with available sales bandwidth. Churn patterns hiding in plain sight. The data already knows. Bruin is learning to tell you before you ask. The Wild West Warning: A Framework for Agent Governance Burak introduces what might be called the Do You Know Where Your Agents Are test. As organizations deploy more and more autonomous agents, the risks compound fast if data access is uncontrolled. His governance framework: Run data quality checks at onboarding before the agent ever touches live data.Assign read-only permissions scoped to exactly what each agent needs.Use agent-controlled outputs (one agent checks another agent's answers before they surface to users).Set hard spending limits per query so no agent can run a runaway Snowflake job.Control internet access permissions per agent, per channel, per use case.The punchline: if your agent only has read access to two marketing tables, the blast radius of any mistake is tiny. Structure the permissions right and you can let the agents run free. https://getbruin.com https://www.linkedin.com/in/burakkarakan/ ⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠ ⁠⁠https://aiforfounders.co⁠⁠ ⁠⁠⁠https://kitcaster.com/application ⁠⁠⁠ ⁠⁠⁠https://ryanestes.info⁠⁠⁠

    51 min.
  5. Delightful Procurement: The CFO That Never Sleeps - Alex Yakubovich from Levelpath

    2 APR

    Delightful Procurement: The CFO That Never Sleeps - Alex Yakubovich from Levelpath

    Procurement Is Not Boring. It's Just Broken. There is a word that kills deals before they start. A word that makes investors yawn, makes journalists skip the story, and makes founders steer away from the category entirely. That word is procurement. Alex Yakubovich has spent his entire career proving that instinct wrong. As co-founder and CEO of Levelpath, and previously co-founder and CEO of Scout RFP (acquired by Workday for $540 million in 2019), Alex has made procurement his life's work. Not because it is glamorous. Because it is genuinely broken. And because broken things, when fixed well, are worth a fortune. This episode covers what it actually means to build an AI native company from the inside out, why delightful procurement is a real mission and not a marketing tagline, and what founders building in any category can learn from a man who took the most overlooked function in business and turned it into a $100M+ venture-backed platform. The Anchor Framework: What Doesn't Change Alex opened by addressing the thing that keeps most founders anxious right now: the pace of change. His answer was not to slow down or resist it. It was to find the anchors that hold steady underneath all the noise. At Levelpath, those anchors are their four values: Obsess over the customer (the north star above all others)A players only (owners, not passengers)Elevate our employees (growth that sometimes comes with pain, and is always worth it)Earn the trust of others (not just "have integrity," but actively earn it, every single day)The insight here is structural. When everything else is accelerating, values are not motivational posters. They are operating instructions. They tell every person in the company what to optimize for when no one is watching. The Experiment Framework: Run More, Not Fewer Counterintuitively, Alex argued that the right response to AI-driven chaos is not more focus. It is more experimentation. The cost of experiments has collapsed. What used to take two weeks of spreadsheet warfare now takes seconds. That changes the calculus entirely. But the filter for which experiments to keep? That never changes. His rule: name the customer this experiment will serve better. If you cannot answer that question with a specific person in mind, kill it. If you can answer it clearly, run it. The Delight Framework: Predictable, Not Surprising Alex built his case for "delightful procurement" not on feature lists or dashboards, but on a feeling. The highest compliment Levelpath receives from customers is: "This is the product I would have built if I were a product person." That is not a UX win. That is empathy at scale. His practical examples of delight in enterprise software: Label your icons. Or remove them entirely. Cognitive load kills trust.Pre-configure the AI assistant to deliver an insight the moment someone lands on a page, before they ask. (A negotiation strategy based on your company's playbook, generated automatically when you open a contract, is a delight.)The Pavlovian ping. DocuSign's signature sound. Quicken's completion tone. Small audible moments that signal: you did something right.The through line is predictability. Delight is not surprise for its own sake. It is when the product does exactly what you needed before you knew to ask for it. https://www.levelpath.com https://www.linkedin.com/in/alex-yakubovich/ ⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠ ⁠⁠https://aiforfounders.co⁠⁠ ⁠⁠⁠https://kitcaster.com/application ⁠⁠⁠ ⁠⁠⁠https://ryanestes.info⁠⁠⁠

    59 min.
  6. Your API Keys Are Killing Your Productivity: Mitchell Jones of Lava.so

    1 APR

    Your API Keys Are Killing Your Productivity: Mitchell Jones of Lava.so

    Mitchell Jones did not set out to build a payments company. He set out to solve a problem he could not stop running into: brilliant people paralyzed by plumbing. The API keys, the secret credentials, the subscription walls, the context switches. Every one of those friction points is a tax on thinking, and Mitchell decided the tax was too high. Lava is his answer. At its core, it is an AI gateway that sits between end users and the services they need, handling authentication, payment, and routing so the human never has to. Install the Lava MCP, load your wallet, and your Claude Code or Codex instance can immediately reach financial data, LLM models, go-to-market enrichment tools, blockchain queries, and dozens of other paid APIs without a single secret key or signup flow. The Two-Sided Marketplace Framework Lava operates on a marketplace model with two distinct customer types, each with a distinct problem Lava solves: End users: founders, operators, and builders who want to access paid services without managing credentials or subscriptions. Lava handles the plumbing and acts as their universal AI service wallet.Merchants and service providers: companies sitting on valuable APIs and data who have no native way to meter, monetize, or convert the agent traffic already hitting their endpoints. Lava becomes their monetization layer, tracking usage, enforcing paywalls, and remitting payments, without requiring any new infrastructure.The Manager-of-Instances Framework Mitchell introduced a framework that reframes the exhaustion founders feel after long AI work sessions. The shift from individual contributor to manager is not metaphorical. When you run multiple Claude Code instances simultaneously, you are no longer doing the work. You are directing it, context switching constantly, evaluating outputs, making judgment calls. The mental load is managerial, and it compounds quickly. Recognizing that shift is the first step toward managing your energy alongside your instances. The Systems Over Goals Framework Mitchell's team at Lava does not set goals for how they adopt new AI tools. They set systems. Teammates experiment freely, share their wins and learnings weekly, and those learnings get baked into default files, memory blocks, and shared context that the entire org benefits from automatically. The system compounds. The goal-setting would not. Founder AI Experiment Using Cursor or Claude Code with the Lava MCP installed, build a one-prompt podcast production workflow. Start by listing every tool in your current production stack that has an API. Then prompt Claude to check which of those tools are already accessible through the Lava gateway. For any that are available, write a single system prompt that takes a recording timestamp as input and chains all the downstream production tasks: transcription, show notes generation, title creation, and asset formatting. Time how long the workflow runs versus your current manual process. This gives you a real cost-per-episode number and a live demonstration of the "one-shot your whole tech stack" concept Mitchell describes. https://www.lava.so/ https://www.linkedin.com/in/mitchell-jones-333559a2/ ⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠ ⁠⁠https://aiforfounders.co⁠⁠ ⁠⁠⁠https://kitcaster.com/application ⁠⁠⁠ ⁠⁠⁠https://ryanestes.info⁠⁠⁠

    52 min.
  7. The New SEO Is Happening Without You

    31 MRT

    The New SEO Is Happening Without You

    The theme of Q1 2026 is "I feel behind." Every founder, from the scrappy solo operator to the venture-backed exec, is feeling the same anxiety. Site traffic is dropping. AI chatbots are answering questions that used to send customers to your website. And the brands that wait to figure this out are not just losing clicks. They are losing the narrative. Justin Inman spent nearly a decade at Google selling enterprise ad tech to the world's largest marketers, companies like Coca-Cola, L'Oreal, and Unilever. He watched those companies resist the digital shift, then scramble to catch up. When he left Google and started watching his own AI usage explode, and then noticed his mom casually quoting ChatGPT, he knew something bigger was coming. He got demos of the biggest players in the AI visibility space. One demo was so underwhelming, so narrow in its thinking, that he started his own company the very next day. That company is Emberos, and it is building what Justin calls the operating system for AI brand visibility. The core insight Justin brings to this conversation is deceptively simple: your brand exists inside AI systems right now, and you have zero control over what those systems are saying about you. Every LLM, from ChatGPT to Gemini to Perplexity to Grok, has formed its own opinion about your pricing, your product, your genre, your identity. And those opinions are often wrong. Emberos measured 500 brands and found that 90% have factual errors across at least one major language model. One small studio picked up a festival film that every AI in the world had mislabeled as a violent thriller with a John Wick comp. Nobody dies in that movie. It took four weeks of strategic fix packs to correct the narrative. The Framework: Paid, Owned, and Earned Across the AI Layer Justin's central argument is that most players in the AI visibility space are thinking too small. Their answer to the problem is to publish more AI-generated content, flood syndicated publishers, and hope the LLMs pick it up. Justin calls this "push to publish," and he says it is not only ineffective, it is dangerous. LLMs will get smarter. The brands that played these hacks six months ago are already getting delisted. Emberos takes a fundamentally different approach, mapping AI visibility across the full digital footprint: Paid: Connected TV ads, programmatic spend, and paid placements all feed signals into AI systems. Emberos is running live studies with major streamers to measure the correlation between TV exposure and generative AI search behavior.Owned: Your website, FAQs, schema markup, and YouTube captions all contribute to how LLMs read and cite your brand. If your site is not structured for LLM readability, it is invisible to the systems now acting as the front door of the internet.Earned: Podcasts, PR placements, influencer content, and press all create citations inside AI systems. Remarkably, Emberos now recommends podcast appearances as a strategic fix pack for brand visibility, because podcast transcripts are among the cleanest, most credible training data available to LLMs.https://emberos.ai/ https://www.linkedin.com/in/jinman11/ ⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠ ⁠⁠https://aiforfounders.co⁠⁠ ⁠⁠⁠https://kitcaster.com/application ⁠⁠⁠ ⁠⁠⁠https://ryanestes.info⁠⁠⁠

    58 min.
  8. Enterprise AI for $20 a Month

    31 MRT

    Enterprise AI for $20 a Month

    The dream of AI that actually knows your business has been haunted by two enemies since day one: hallucinations and speed. Everyone building on top of large language models has accepted these as the cost of doing business. Ankit Dheendsa, CTO of Morphos.ai, decided not to. Ankit came on to build something that most of the industry had already written off as impossible: a vectorization engine that doesn't just compress data, it fundamentally rethinks how a vector is constructed. The result is Green Vectors, Morphos's patent-pending core technology that reduces vector database size by up to 99.5%, speeds up queries by 4x, and pushes search accuracy to the 99th percentile. That's not an incremental gain. That's a category redefinition. What makes this sticky for founders is the affordability story. Enterprise RAG used to cost $50,000 and up, delivered mediocre accuracy, and still left your team second-guessing every output. Morphos.ai now delivers that same infrastructure layer for $20 a month, the same price as ChatGPT Pro, with dramatically better performance across the board. https://www.morphos.ai https://www.linkedin.com/in/ankit-dheendsa/ ⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠ ⁠⁠https://aiforfounders.co⁠⁠ ⁠⁠⁠https://kitcaster.com/application ⁠⁠⁠ ⁠⁠⁠https://ryanestes.info⁠⁠⁠

    1 u 2 m
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AI for Founders is where 47,000+ founders learn to build and scale with AI. Hosted by Ryan Estes, a Denver investor, creator, and founder, the show breaks down real strategies from top operators and AI visionaries. AI-ready data, zero-dependency workflows, founder-led distribution, and the tools driving revenue for today’s fastest-growing companies. If you’re a technical or non-technical founder who wants to work smarter, scale faster, and stay competitive, this podcast is your weekly unfair advantage.

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