AI for Founders with Ryan Estes

A 6-person team just built in 3 months what took 18 months and $2 million the last time around. That's not a productivity story. That's a story about the ground shifting under an entire trillion-dollar industry.

Tim Lidman knows consulting from the inside. He grew up in the collaboration industry: Webex before Cisco bought it, SuccessFactors before SAP bought it, then a decade helping run ThinkTank, the structured collaboration platform that Big Four firms used to run client workshops. When Accenture acquired ThinkTank's assets in 2021, Tim spent about four years operating at the partner level inside one of the biggest consulting machines on Earth. And what he saw was a workflow begging to be rebuilt: humans designing engagements, humans facilitating, humans synthesizing, and one poor analyst up until 2 AM cobbling together the PowerPoint.

So he built Clyde, which launched April 7, 2026 at meetclyde.com. Clyde is what Tim calls AI-native collaboration: a workspace where you bring a real problem, collaborate with a library of AI advisors that act like human experts, pull in actual human stakeholders, and walk out with an aligned outcome and a usable deliverable. Not a wrapper. Not a chatbot bolted onto a legacy whiteboard. A guided system that extracts your true intent, because as Tim puts it, 99% of users don't know what they don't know about prompting.

The results are early but loud: 1,300 users in the first month on a pure product-led growth motion, a fast-follow release shipping in June with adaptive workflows, and a customer base that already includes third grade teachers walking away with McKinsey-level curriculum plans. Everyone's getting a raise. Thanks, Clyde.

Ryan and Tim also go deep on the founder condition in 2026: the guilt of stepping away from your desk, scheduling dedicated slots for original thought because AI can't invent new information, developers mourning the flow state as they become project managers of agent fleets, raising with extreme caution in a VC landscape where a Series A is the new pre-seed, and why Tim's kids get zero screen time while their dad builds frontier AI. Plus: heavy metal drumming, Suno experiments with his daughter Chloe, why Lovable's Anton Osika is the founder Tim admires most, and a charity using pediatricians as a distribution network.

⁠⁠https://meetclyde.com

⁠⁠https://linkedin.com/in/timlidman

⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠

⁠⁠https://aiforfounders.co⁠⁠

https://inboxalchemy.co/

https://ainativestudent.com/

Jason Katz popped his LCL three weeks ago scrambling out of a leg entanglement, and honestly, that injury is the whole episode in miniature. You get excited, you move fast, you stand up into a hold you did not see, and something structural gives. In this What It Do check-in, Jason (co-founder of Kindling Solutions, back for another round) and Ryan trade war stories from the two weeks that turned both of their companies from build mode into go mode.

Jason drops the concept that should be tattooed on every founder's forearm in 2026: built is not built to scale. Anyone can vibe code something that "works" in a single session now. Jason points to reports of vibe coders getting sued after losing company data through open-ended permissions, and he watches CEOs get star-gazed by AI demos, fire people AI cannot actually replace, then quietly rehire them. Kindling's answer is a governance layer: a way to let a client's internal Lovable and Claude Code tinkerers keep building while Kindling governs the builds it does not even touch. Operators first, then engineers. Jason claims his team has exited three companies at nine and ten figure outcomes, and that operating scar tissue is the product.

Ryan, meanwhile, is in full sales dog mode. He restructured his entire week (calls only on Tuesdays and Thursdays, deep work on Monday, Wednesday, Friday) and promptly signed two newsletter sponsorship deals in one week: Momentous, the NSF Certified supplement company behind the grass-fed whey and creatine he already takes daily, and Taelor, the AI-plus-human-stylist menswear rental subscription that is about to give him a dramatic before-and-after wardrobe glow-up, per his daughter's demands.

And the whole thing wraps with the two of them planning a Denver panel with Gabe Anderson and ID345's Danny Newman that may or may not end in a staged WWE brawl. Leopard Speedo has been threatened.

• https://kindlingsolutions.com
• https://taelor.style
• https://www.livemomentous.com
• https://aiforfounders.co
• https://www.linkedin.com/in/jasonkatz99/
• https://www.linkedin.com/in/estesryan/

Most founders think the endgame is IPO or bust. Todd Furniss built a company that offers a third door: sell to someone who keeps your entire team, hands leadership three to five year employment agreements, and then drops patented AI into your operations like a turbocharger into a truck that's been stuck in third gear for a decade.Todd is the CEO and co-founder of AI Squared, formally AIAI Holdings Corporation, publicly traded on the Nasdaq under the ticker AIAI since May 14, 2026. The model is deliciously simple to say and brutally hard to copy: buy real operating companies with real revenue and real EBITDA, retain the management teams, and deploy what Todd describes as Nobel-nominated Transformational AI to create new products, amplify earnings, and redefine what the business can become. No pilots. No rip and replace. No layoff bloodbath. Todd says nearly a billion dollars of EBITDA is sitting in the acquisition pipeline, and here's the kicker: AI Squared didn't cold-call a single one of those companies. They all came knocking.In this conversation, Todd pulls back the curtain on why he listed in Dallas instead of New York, why a direct public offering democratizes AI upside for retail investors, why the scariest businesses are the best businesses, and why 600 years of economic history says the AI jobs panic has it exactly backwards. He also explains how behavioral psychometrics turned a construction company's bid estimator into a weapon, and why he told his kids the liberal arts just became the most valuable degree on campus.https://aiaiholdings.comhttps://skullgames.orghttps://aiforfounders.cohttps://inboxalchemy.co

The same artificial intelligence saved one insurer a billion dollars and cost another two billion. Same tool. Opposite outcomes. The only variable was who the machine was actually working for.

That single tension is where this episode opens, and it turns out to be the question that quietly decides everything a founder builds. Gaurav Agrawal, Vice President of Engineering at Collective Health, has spent a career standing at the exact moment technology flips from impossible to inevitable. He was in the Apple atrium when Steve Jobs revealed the iPhone and watched the room's jaws hit the floor. He helped Reliance Jio connect 18,000 villages and vault India from 150th in the world for broadband penetration to first in a matter of months. Now he is pointing that same instinct at the most broken machine in America: healthcare.

What makes this conversation land is that Gaurav refuses the easy framing. AI is not good or evil in healthcare, he argues. It is a mirror. Point it at margin and you get claim denials at machine speed. Point it at the member and you get a 24/7 companion that answers "why was my claim denied" in plain language, a copilot whispering the right answer into a service agent's ear so they can drop the robotic script and actually be human, and a roadmap that arrives in months instead of years. At Collective Health, the rule is blunt: every AI decision starts from "how does the customer benefit." If it also saves money, that is icing on the cake, never the recipe.

The episode gets personal, and that is where it earns its rating. Gaurav's mother fell ill after moving to the US. The best healthcare system in the world, the one he trusted, failed her. He flew her back to India for care. She is no longer with us. That loss is the engine behind his work, and you can hear it. For founders, the practical payload is just as sharp: the benefits trap that springs the moment you hire your tenth person, the places AI absolutely should not go (claim rejections still pass through human eyes, every time), and how a lean team of around 210 engineers compresses an 18-to-24-month roadmap into six.

https://collectivehealth.com

https://aiforfounders.co

https://inboxalchemy.co

Two founders sit down on a Friday with the World Cup playing in the background, and within ten minutes one of them casually reveals he has built a version of himself that works while he sleeps.

That is the hook, and it is not hype. Jason Katz, co-founder of Kindling Solutions, walks through what he calls his personal content machine: a chain of Notion databases, AI agents, and approval triggers that takes a single spoken idea and turns it into finished video, social posts, and carousels, all before he sits down at a computer. The genius is not the automation. Plenty of people automate. The genius is that the output sounds exactly like Jason, because the system is engineered around authenticity instead of around shortcuts.

Here is the part that should make every founder lean in. Jason does not let the AI write his ideas. He lets the AI interview him. He talks into his phone in the backyard with a coffee, an interviewer agent trained on the tactics of Joe Rogan, Oprah Winfrey, and Howard Stern pulls his real takes out of him across ten to twelve questions, and only then does the structuring begin. The words are his. The machine just gives them shape. As he puts it, the context truly does half the work, and that is the line nobody is saying out loud.

Meanwhile Ryan turns the conversation into a masterclass on performance itself. After more than a thousand podcasts, he has reduced great content to a few unglamorous truths: sleep and caffeine are the real production stack, clarity beats cleverness, lead with a current event so your guest can find their feet, and tell yourself to speak ten percent slower so the ums take care of themselves. It is the kind of advice that sounds obvious until you realize almost nobody actually does it.

Both threads land on the same destination. First-time founders obsess over product. Second-time founders obsess over distribution. Jason and Ryan are both, by their own admission, finally crossing that line, moving from "what is this business" to "let the world know what is up." The episode is the sound of two operators getting comfortable being the face of the thing they built.

⁠⁠https://kindlingsolutions.com

⁠⁠https://aiforfounders.co

⁠⁠https://linkedin.com/in/jasonkatz99/

⁠⁠https://linkedin.com/in/estesryan/⁠⁠

The same artificial intelligence saved one insurer a billion dollars and cost another two billion. Same tool. Opposite outcomes. The only variable was who the machine was actually working for.

That single tension is where this episode opens, and it turns out to be the question that quietly decides everything a founder builds. Gaurav Agrawal, Vice President of Engineering at Collective Health, has spent a career standing at the exact moment technology flips from impossible to inevitable. He was in the Apple atrium when Steve Jobs revealed the iPhone and watched the room's jaws hit the floor. He helped Reliance Jio connect 18,000 villages and vault India from 150th in the world for broadband penetration to first in a matter of months. Now he is pointing that same instinct at the most broken machine in America: healthcare.

What makes this conversation land is that Gaurav refuses the easy framing. AI is not good or evil in healthcare, he argues. It is a mirror. Point it at margin and you get claim denials at machine speed. Point it at the member and you get a 24/7 companion that answers "why was my claim denied" in plain language, a copilot whispering the right answer into a service agent's ear so they can drop the robotic script and actually be human, and a roadmap that arrives in months instead of years. At Collective Health, the rule is blunt: every AI decision starts from "how does the customer benefit." If it also saves money, that is icing on the cake, never the recipe.

The episode gets personal, and that is where it earns its rating. Gaurav's mother fell ill after moving to the US. The best healthcare system in the world, the one he trusted, failed her. He flew her back to India for care. She is no longer with us. That loss is the engine behind his work, and you can hear it. For founders, the practical payload is just as sharp: the benefits trap that springs the moment you hire your tenth person, the places AI absolutely should not go (claim rejections still pass through human eyes, every time), and how a lean team of around 210 engineers compresses an 18-to-24-month roadmap into six.

https://collectivehealth.com

https://aiforfounders.co

https://inboxalchemy.co

⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠

https://ainativestudent.com/

Your blood pressure spikes the moment the cuff goes on. You're sitting on crinkly paper in a cold exam room, and the number on the screen may say more about the moment than your everyday life. It's the classic "white coat effect," and it doubles as a metaphor for one of healthcare's biggest challenges: we often measure people at isolated moments instead of continuously, then wonder why better outcomes remain elusive.

Amir from Hello Heart spends his days closing that gap. Hello Heart is a preventive heart health platform built around a connected blood pressure monitor, a smart pill organizer, and a mobile app that helps members better understand and manage their cardiovascular health. Today, Hello Heart partners with more than 150 leading employers, national health plans, and labor organizations, supporting millions of eligible members while helping organizations improve cardiovascular health outcomes through AI-powered prevention.

The newest addition is Nia, launched in October 2025 as the world's first AI heart health assistant. Designed to complement, not replace, clinical care, Nia helps members better understand their heart health, stay engaged with their care plans, and prepare for more informed conversations with their healthcare providers. This episode is a rare founder conversation that goes beyond the product demo. If you're building vertical AI, healthcare technology, or any AI system where trust and accuracy matter, this is one worth studying.

The thread running through the entire conversation is trust. Amir returns to a simple idea: people were never meant to be the primary data layer. AI works best when it reduces administrative burden, surfaces meaningful insights, and helps members stay engaged between clinical visits, giving healthcare professionals more time to focus on the conversations that require empathy, judgment, and human expertise. He calls it the shift from reactive to preventive care, and he's clear that earning trust requires thoughtful design, rigorous guardrails, and a deep commitment to responsible AI.

https://www.helloheart.com

https://www.linkedin.com/in/amir-dolev-b5618421/

https://www.helloheart.com/press/hello-heart-launches-the-worlds-first-ai-heart-health-assistant-nia

https://ainativestudent.com

⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠

⁠⁠https://aiforfounders.co⁠⁠

https://inboxalchemy.co/

A stranger gives you ten seconds. Before you open your mouth, before the pitch, before the handshake, they have already read your shirt and filed you away. Zoher Karu thinks that ten seconds is a data problem, and he left one of the biggest data jobs in tech to go solve it.

Zoher spent years as Global Chief Data Officer at eBay and Chief Data and Analytics Officer at Blue Shield of California. Now he is Head of AI at Taelor, the AI-powered menswear rental subscription founded by Anya Cheng and Phoebe Tan. The premise is simple and a little radical: most men do not have the time, the skills, or the desire to shop, yet they still want the outcome of looking sharp. So Taelor sends you a box, you wear it, you keep what hits, you mail back the rest, and no one ever folds laundry or guesses at the mall again.

Underneath the box is the hard part. Zoher calls it the matching problem. Picture Ryan, 30,000 pieces of inventory, and the question "which six go in the box." Basic rules thin the herd, no wrong sizes, no shirts you would hate. After that, you need to capture something almost nobody can write down: why a person on the street simply looks put together. Ask a great stylist to explain the rule and they cannot, the same way a driver cannot list every reason they tap the brake. Taelor's job is to bottle that instinct and run it at scale, with human stylists in the loop and the machine learning from every piece of feedback.

The twist that should make founders sit up is the second business hiding inside the first. Every rental generates a signal about what real men actually like on real bodies in real contexts. Brands today buy on gut, betting that yellow is big this year. Taelor is building the feedback layer that turns a B2C rental into a B2B data product for the brands themselves, with sustainability as the upside, since roughly 30% of clothing reportedly reaches the landfill never having been worn.

This one is for the founder who spent on the camera and the mic and still shows up in a college shirt. Your product may be great. In the first ten seconds, you are the product.

https://taelor.style/

https://taelor.style/pages/membership

https://www.linkedin.com/in/zzkaru/

⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠

⁠⁠https://aiforfounders.co⁠⁠

https://inboxalchemy.co/

https://ainativestudent.com/

Every month your inference bill climbs, and you tell yourself it is the cost of doing business. What if it is actually a tax on what you do not know? In this episode, the founder of Featherless makes a blunt case: the best model for most of what your startup does is open source, often runs for basically peanuts, and is frequently built in China. He has put real money behind that thesis, about $25M across a seed and a Series A led by AMD Ventures and Airbus Ventures, and a platform that holds tens of thousands of open models online at once through a single API key.

The throughline is freedom. Eugene's grandmother speaks seven languages and none of them are English or Chinese, which is roughly half the planet that the closed, English-and-Chinese-first future would leave behind. Open source, he argues, is not just free as in money. It is free as in freedom: when the model runs on your terms, nobody can ever take it away from you. He walks through why the database wars of the past, Oracle and Microsoft and IBM, then MySQL and Postgres, are replaying in AI at ten times the speed, why "lazy" models are really just a mirror of us, and why the labs chasing superintelligence may be solving the wrong problem while businesses quietly beg for one thing: reliability.

⁠⁠https://www.featherless.ai
⁠⁠https://www.x.com/picocreator (Eugene on X)
⁠⁠https://www.techtalkcto.substack.com (his Substack, Tech Talk CTO)
⁠⁠https://www.wiki.rwkv.com (RWKV, Linux Foundation)

⁠⁠https://www.linkedin.com/in/estesryan/⁠⁠

⁠⁠https://aiforfounders.co⁠⁠

https://inboxalchemy.co/

https://ainativestudent.com/

There is thirty billion dollars a year in lost rent sitting in empty units across America, and that vacancy quietly erases roughly half a trillion dollars of property value. Everyone assumed the fix was price, amenities, or a slicker chatbot. Then Nick Deveau and his co-founder Ben Epstein got their hands on millions of real leasing calls from one of the largest apartment owners in the country, pointed a team of machine learning engineers at the data, and found something nobody scripted for. The single strongest predictor of a signed lease was not the special, not the square footage, not a scarcity tactic. It was whether the leasing agent laughed on the phone. The second strongest was whether they asked a genuinely curious question.

So Grotto AI did the counterintuitive thing. While most of the industry raced to replace humans with voice agents, Grotto built a tool to make humans better at the one thing only humans can do: build rapport. A leasing agent gets a push notification fifteen minutes before a tour telling them the prospect has a dog named Fido, loves natural light, and drives a Subaru. They record the tour on a small clip-on mic, get instant feedback on what they crushed and what they missed, and Grotto drafts the personalized follow-up, catches the special they forgot to mention, and quietly does the CRM grunt work. Nick calls it targeted advertising for the real world. Ryan called it a second brain for the field. Both are right.

This episode is the clearest case study going for vertical AI: pick one painful, measurable leak, capture data nobody else has, and sell revenue instead of cost cuts.

https://grotto.ai

https://www.linkedin.com/in/nick-deveau-a6241379/

https://www.linkedin.com/in/estesryan/

https://aiforfounders.co

https://inboxalchemy.co

https://robinhood.org