AI for Founders with Ryan Estes

Every founder has a graveyard. Half-finished apps, abandoned prototypes, that "killer tool" you vibe-coded over a weekend and never touched again. Mariam Hakobyan, Co-Founder and CEO of Softr, thinks she knows exactly why those projects keep dying, and it is not your discipline. It is the chains. AI handed everyone a hammer and called them a carpenter, but it never removed the hard part. It just moved the complexity onto you: the authentication, the permissions, the security, the thousand boring edge cases that make a toy into a tool people can actually log into.

Mariam is an engineer turned entrepreneur who led product and engineering teams of forty-plus people before walking away from a six-figure job to build something of her own. She and her husband Artur Mkrtchyan started Softr in 2019 with one stubborn belief: 80% of every business app is the same repetitive plumbing, and nobody should have to rebuild it from scratch ever again. They call it Lego for software. Connect your data, snap the blocks together, and a non-technical operator ships a full, secure, working app in about five minutes.

The numbers tell a quiet, brutal story. A $2.2M seed they did not even plan to raise. A $13.5M Series A from FirstMark. Then a hard stop on fundraising, because the thing was already profitable. Today Softr runs eight-figure revenue with a lean team of fifty across fifteen countries, no traditional sales team, and growth that came almost entirely from a Product Hunt launch and word of mouth. Oh, and investors told a husband-and-wife founding team it would never work. Mariam's reply: they had a decade of conflict-resolution experience before they ever incorporated.

This episode is for the founder who keeps starting and never shipping, the operator drowning in spreadsheets, and anyone trying to figure out when to reach for Claude Code and when to put the terminal down.

https://www.softr.io/pricing

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

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

⁠⁠https://aiforfounders.co⁠⁠

https://trynina.co/

Two fifteen-year-old rock climbing buddies from Long Island made a pact in a surf lineup: build one-of-a-kind experiences for causes they cared about. That idea died. So did the loyalty app before it, and the apparel company after it. What survived was the thing nobody planned, an agency born from following opportunity instead of forcing a vision.

Justin Abrams and Mike Rispoli have been failing forward together for twenty years, and Cause of a Kind is the compounding result. The deal that let them quit their jobs was the Hospital for Special Surgery in New York, their first real foray into medical software and the moment Justin took out a half million dollar SBA loan and burned the boats. Today they build and modernize software for small and mid-sized businesses on a flat monthly model, no offshore handoffs, no surprise invoices.

This conversation is a gut check for every founder currently drowning in shiny object syndrome. Mike has the scars of the Web3 era and sees the exact same pattern repeating with AI: companies slapping an "AI native" label on a context call to ChatGPT, then wondering why three competitors clone them in a month. His thesis is sharp and survivable. The magic is not AI. The magic is AI plus workflow plus deep domain knowledge, the combination that cannot be knocked off because you had to be the person on the inside to build it.

Then there is the distribution story, which is the part founders will rewatch. Cause of a Kind went from roughly 7,000 to 160,000 plus YouTube subscribers in five months by doing one unglamorous thing relentlessly: they ship every single day. No filter, no precious production cycle, just two fast-talking Long Islanders who treat their business as a media house and treat publishing as the cheapest sales conversation on earth.

https://www.causeofakind.com/

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

https://www.linkedin.com/in/michael-rispoli-cto/

https://www.youtube.com/channel/UCWAstEyCK6YsKVTTRsQr37w

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

⁠⁠https://aiforfounders.co⁠⁠

https://trynina.co/

Some of the smartest engineers alive are designing the physical world with software older than their interns. Brake discs, axles, medical devices, aircraft, all built on tools that can take twenty minutes just to open a file, and a knowledge base that walked out the door when the industry shipped its expertise overseas. Hugo Nordell saw this up close. Trained as a theoretical physicist, seasoned in Silicon Valley's drone and autonomous driving years, then a digital transformation executive at Sandvik and Aker, he kept watching brilliant hardware teams fight their own tooling on a daily basis while production costs quietly ballooned.

So he built the thing he wished he had. Encube is a browser based, collaborative design platform that sits between your CAD system and your release management, then layers AI on top of a foundation almost nobody else is building: a deterministic engine that actually understands manufacturability. Think of it as a FigJam board on steroids, where complex CAD models and heavy engineering drawings become first class citizens, loading in two to three seconds on a run of the mill laptop with no expensive graphics card required. People thought he was cheating. He was not.

The deeper insight is the one founders in every category should tattoo somewhere visible. Generative AI is rewriting software engineering because software has forty years of validation infrastructure: compilers, linters, unit tests, CI/CD, stack traces that let an agent self correct. Hardware has none of that. There is no compiler for atoms. So Encube is building one, on the GPU, blazingly fast, deterministic where it must be, with large language models bolted on only at the edges where stochastic answers are safe. Get that order right, and you could reimagine hardware design the way Lovable, Bolt, and Claude Code reimagined software. Get it wrong, and you ship slop into the one place slop kills people.

https://www.getencube.com

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

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

⁠⁠https://aiforfounders.co⁠⁠

https://trynina.co/

That is the dirty secret of LinkedIn for most founders. You scroll, you cringe at the textbook-perfect ChatGPT posts, you maybe drop a like, and you log off feeling exactly as broke as when you opened the app. Ali Hafizji, founder and CEO of Wednesday Solutions and the builder behind Chime at getchime.co, has been quietly running a different playbook. He went from under 5,000 LinkedIn followers to 12,000 without leaning on a content engine. The trick was not posting more. It was commenting smarter, on the right posts, in front of the right tribe, every single day.

In this episode, Ali breaks down why the comment section is the most underutilized lever in B2B SaaS, why your ICP does not want to be educated by you, and how he built an AI agent that does the soul-crushing work of finding the right LinkedIn conversations so you can show up, drop one human comment, and close the laptop in ten minutes. He also walks through the design partner pricing, the rare anti-predatory SaaS model where the price goes down as the user base grows, and the curation logic behind Chime's 40,000-influencer database. If you have ever felt invisible on LinkedIn while watching your competitors print pipeline, this one is for you.

The Interest Graph Engagement Loop

• Engage on posts that match your expertise.
• Show up in the feed of your ICP automatically, because LinkedIn is an interest graph, not a follower graph.
• Get DMs and conversations started by people who already trust your thinking.
• Skip the cold outreach phase entirely.

The Comment Quality Bar

• No teaching, no textbook tone, no "ChatGPT wrote this for me" energy.
• Lead with contrarian views framed without picking a fight.
• Add wordplay, wit, or one personal anecdote.
• Keep it to two or three lines.
• Always ask the author a question.

The Post-Comment DM Loop

• DM the author of the post you commented on.
• DM other people who engaged in the comments.
• No agenda, just "coffee chat" energy.
• Invite them to your newsletter once trust is built.
• Send referrals their way and watch the favor return.

The Anti-Predatory SaaS Pricing Model

• Lock in $39/month forever for the first 25 design partners.
• Add new data sources (Reddit, X) without raising the base price.
• Pass cost savings down to customers as the user base grows, not up.

https://getchime.co

https://aiforfounders.co

https://linkedin.com/in/alihafizji/

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

Your IT team is drowning. Every "how do I get access to..." Slack message you fire off is making it worse. And while everyone in 2026 is busy debating whether AI is coming for the C-suite, Tom Bachant has spent the last four years quietly automating the layer of work that actually keeps companies running. The help desk. The ticket queue. The Jira dashboard you've been ignoring for three weeks hoping it disappears.

Tom is the co-founder and CEO of Unthread, an AI-powered helpdesk built natively into Slack and Microsoft Teams. He's also a two-time founder who sold his first company, Dashride, to Cruise in 2018, lived through the Cruise unraveling, then went back into the trenches with Y Combinator's Summer 2022 batch. His new company has raised $3.5M, landed Intuit, Lemonade, and Automattic as customers, and finished as a TechCrunch Disrupt 2025 Startup Battlefield Top 20 finalist.

In this episode, Tom walks Ryan through the inception of Unthread, the YC playbook that got him to his first 10 customers without spending a dollar on ads, and the philosophical bet that code is now free so distribution is the only moat left. He also explains, with a straight face, why he runs abolishcars.org as a side project despite his first company being a ridesharing platform.

The conversation kicks off with cars (Tom hates them, Ryan rides fixed gear, they both agree on flipping people off responsibly), and ends with downhill mountain biking in Crested Butte. In between, you get one of the cleanest tactical breakdowns of agentic service management you'll hear all year.

https://unthread.io

https://aiforfounders.co

https://abolishcars.org

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

https://inboxalchemy.co

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

https://trynina.co/

Picture St. Louis, 1849. Two men with a bottle of champagne and a brutal choice: go north for beaver pelts, or go south chasing gold in California. Joris Delanoue does not even blink. He picks the gold. Not for the metal. For the belief.

That single line tells you everything about this conversation. Joris, co-founder and co-CEO of Fairmint, has spent the better part of two decades pushing into frontiers nobody else wanted to settle. He sold a cloud computing company, Nexteem, before most people trusted the cloud. Now he is doing the same thing to the one document that quietly governs every startup's destiny: the cap table.

Here is the uncomfortable truth he lays out. Fifty years ago, Microsoft went public at an eight hundred million dollar valuation, and ordinary people built entire retirements on the climb that followed. Today, companies stay private until they are worth five hundred billion, and the upside goes to a happy few. The frontier did not close. It just moved behind a velvet rope. Joris wants to tear the rope down, and he thinks the tool to do it is equity that lives on a blockchain: programmable, transferable, and liquid enough that the engineer who bet fifteen years of her life on a startup can actually borrow against her shares to buy a house.

This is a conversation about wealth, about who gets to build it, and about why the most defensible thing you own in the AI era is no longer your product. It is your distribution. Buckle up.

Compliance by Automation (not Intermediation)Joris frames the entire Fairmint thesis as a shift away from people and toward code.

• Old world: compliance happens through layers of intermediaries, lawyers, banks, and reconciliation.
• New world: compliance lives inside a smart contract that mimics securities law and applies the rules automatically.
• The promise: lower transfer costs, fewer trolls under the bridge, and a single source of truth for who owns what.

The Shovel Seller's DilemmaThe gold rush metaphor that opens the episode is a strategy lesson in disguise.

• The prospector grinds sixteen hours a day and often ends with broken backs and empty pans.
• The shovel seller monetizes everyone else's dream regardless of who strikes gold.
• Joris flips it: do not just sell shovels, own a piece of the mine through programmable equity.

https://www.fairmint.com/

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

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

⁠⁠https://aiforfounders.co⁠⁠

https://trynina.co/

https://ainativestudent.com/

Everything you know about getting found online is about to be obsolete. For two decades, founders chased one algorithm. Backlinks, page speed, keyword stuffing, the whole exhausting machine. Then a handful of chatbots quietly took the wheel, and now they decide which businesses get recommended and which ones get ghosted. SEO is bleeding out. Google traffic is leaking. And a huge slice of buyers now ask ChatGPT to pick their solution before a human ever enters the conversation.

Here is the twist that should make every founder sit up straight: the playing field is wide open for the first time in twenty years. Tiny brands are outranking billion-dollar incumbents. Unknown podcasts are beating the giants. The new game is not about who has the biggest backlink pile. It is about who tells the clearest story.

In this return visit, Jenna Hannon, co-founder and CMO of Hatter, runs a live tactical teardown using Ryan's own podcast page as the guinea pig. She walks through what AI search actually rewards, why a lead who found you through ChatGPT shows up on the call already sold, and the exact content structure that gets your brand cited instead of buried. She also drops the unglamorous truth about where AI pulls its recommendations from, and it is probably not where you think.

This is your chance to control your narrative before the bots write it for you.

The Three Names, One Thing PrincipleThe category has a branding problem. AEO, GEO, AI search. They sound different. They are not.

• AEO stands for answer engine optimization.
• GEO stands for generative engine optimization.
• AI search is the plain-language version.
• All three describe the same goal: showing up inside AI chatbots. Ignore the LinkedIn posts insisting they are separate disciplines.

The Magical MomentThe new tell that AI search is working for you.

• A lead arrives having done their research inside a chatbot, not a Google rabbit hole.
• They already know they have a problem and that you are one of the few recommended solutions.
• They land on the call warm, informed, and close to buying.
• The chatbot's endorsement carries trust, because the user already treats the AI like a trusted advisor.

https://gethatter.ai

https://aiforfounders.co

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

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

https://trynina.co/

You are the smartest person in your company. That is exactly the problem.

Every founder hits the same wall. The strategy lives in your head. The taste lives in your gut. The thousand tiny judgment calls that make your company yours live nowhere anyone else can reach them. So your team waits. They wait on your approval, your context, your answer to a question you have answered nine times already. And while they wait, the work does not move.

Joshua Liberson, CEO and co-founder of Dobbin, has spent a career watching this play out. He designed editorial systems for magazines, ran brand and creative at One Kings Lane, and advised a long list of founder-led companies before deciding the bottleneck was always the same: the founder cannot be in every room. Dobbin is his answer. It is a company AI that captures the fifteen-or-so dimensions of an organization, its culture, values, brand, strategy, and objectives, and then delivers that judgment to every person on the team right inside Slack, where the work already happens.

The pitch is deceptively calm. Dobbin is not a creative generator and not a design tool. It is a thinking partner. The designer drops a layout into a channel and Dobbin critiques it against the principles the team itself articulated. The intern asks what to do today. The CEO uses it for high-value strategic thinking. Josh's favorite proof point is a creative agency built around the photographer Mark Seliger, whose Dobbin was assembled from four and a half hours of audio about a forty-five-year career in lighting, composition, and printmaking. The result: a managing director who now answers RFPs in thirty minutes instead of three weeks and seventeen meetings.

Underneath the warm language is a hard claim about modern work. Microsoft estimates 57% of our time goes to coordination, roughly 22 hours of a 40-hour week. Nobody's KPI is "coordinate more," yet that is what the calendar quietly becomes. Josh's fix is not more project management, which he thinks the world already drowns in. It is what his friend Howard calls ambient alignment: the strategy is simply present, in the channel, evolving as the company evolves, so people stop waiting and start shipping.

And he is honest about the banana peels. A great team is a pirate ship, full of brilliant misfits who wither under too much rigidity. So Dobbin is built to bend. It is iterative, never bedrock. It watches where work drifts from the foundation, then proposes amendments the founder can accept or reject. Structure that empowers, not structure that scolds. Or, as Josh puts it through a borrowed line from a Greek philosopher, you never step in the same river twice, because the river is flowing and so are you.

LinksDobbin: https://dobbin.ai

Joshua Liberson on LinkedIn: https://www.linkedin.com/in/joshliberson/

AI for Founders newsletter: https://aiforfounders.co

Ryan Estes on LinkedIn: https://www.linkedin.com/in/estesryan/

Everything you trust online is about to break, and André Ferraz built a company to catch the people breaking it.

Picture a 12-year-old kid riding his bike through Brazil when a stranger points a gun at his face to steal it. That kid grew up with two computer scientist parents, an early love of code, and a peculiar fascination not with building systems but with breaking them. Three decades later, that instinct for thinking like an attacker became the foundation of Incognia, a company now embedded in 1.2 billion monthly active devices and built on a single contrarian belief: your location behavior is the strongest signal of who you really are.

But the road there nearly ended before it began. André moved to the United States six years ago to chase the biggest market, bringing a thriving location-based advertising business with him. Then the pandemic hit. Physical retailers shut down. Revenue collapsed 95% in a single month. The team went from 250 people to 50, keeping only the engineers. Most founders would have folded. André and his co-founders looked at the precise location technology they had spent over a decade perfecting and asked a different question: what else can this do?

The answer was fraud prevention, and it turned out the world needed it desperately. Incognia now serves banks, fintechs, crypto exchanges, and marketplaces, answering one deceptively simple question for every login, transaction, and signup: is this user who they say they are? The results speak loudly. Triple revenue growth. Six times the return on investment delivered to clients. A 100% trial-to-paid conversion rate. And a 180% net dollar retention rate that means customers keep expanding once they see the data.

The conversation gets genuinely unsettling when André lays out the asymmetry of modern fraud. The criminals are professionals, not hoodie-wearing loners. They run 60,000 fake accounts in two days. They factory-reset devices in 30 seconds to dodge detection. They share tools and open-source software while the banks defending against them compete and stay siloed. The money pouring into making deepfakes dwarfs the money fighting them. As André puts it, if you brought him a deepfake detection company, he would not invest, because detection can never outspend generation.

So Incognia plays a different game entirely. Rather than analyzing whether a video is a deepfake, it checks whether the camera feeding that video is even real. Rather than trusting a spoofable GPS coordinate, it fuses Wi-Fi, Bluetooth, cell tower, compass, accelerometer, and gyroscope signals to locate a device down to eight foot accuracy, close enough to separate two fraudsters in different apartments of the same building. The bet is that AI can fake your face, your voice, and your data, but it cannot cheaply fake the real physical world at scale. Make the attack economically unfeasible, and the fraudster moves on.

The episode closes on a vision that goes beyond catching criminals. André imagines a world without buttons, where the hotel TV logs you in automatically, the thermostat already knows your preferred temperature, and the world quietly personalizes itself around you because it recognizes you everywhere. Stop the fraud first, because that hurts. Then make the world more elegant.

The Asymmetry of FraudAndré's core mental model for why defenders are structurally disadvantaged:

• Criminals break rules freely while banks must follow heavy financial and privacy regulation.
• Fraudsters collaborate and share open-source tools while competing banks stay siloed.
• Deepfake generation attracts vastly more capital than deepfake detection ever will.
• The takeaway: never fight on the attacker's terms, find a different angle.

https://www.incognia.com/

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

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

⁠⁠https://aiforfounders.co⁠⁠

https://trynina.co/

Pradnesh Patil spent years as a product leader at Fortune 500 companies bringing in millions in revenue, and every single quarter the same thing kept happening. He would walk into leadership meetings, present data, get hit with the question "why is this number different from last week," and then watch the opportunity window close while his data team spent two months trying to figure it out.

It was not a bad data team. It was every data team. Data work is complicated, the experts get pulled in fifteen directions, and the backlog never shrinks.

So Pradnesh called up Aaron, his co-founder of ten years and a veteran data and ML engineering leader, and they did the same thing they had done a dozen times before: they built something together. Last time it was an autonomous crypto trading bot. This time it was Altimate AI, a company built on a single insight. The bottleneck in enterprise is not engineering talent. It is the gap between the institutional knowledge locked inside your 15 year veteran's head and the new hires who do not have it yet.

They raised on a few slides without writing a line of code, then built a free product that hit a million downloads across 100+ countries. That feedback flywheel turned into an enterprise offering, a second funding round, and Fortune 500 logos. The latest chapter is Altimate Core, an open source agent data engineering harness that now sits at number one on the industry benchmark.

The Four Components of an Agent Harness

• Context: Metadata pulled from across the hybrid data stack, plus the tribal knowledge previously locked in employee heads.
• Governance: Rules, permissions, and access controls that respect regulated industries like healthcare and financial services.
• Tools and Skills: The specific recipes and connectors agents need for specialized data work.
• Infrastructure: Sandbox environments for hundreds of agents to work in parallel without touching production.

The Tribal Knowledge Capture Loop

• The system watches a senior engineer fix a problem and stores how they did it.
• When a less experienced person hits the same issue, the system recalls the fix and recommends it.
• Users can correct the memory when AI picks up the wrong pattern.
• Active coaching of agents is positioned as the new responsibility for senior engineers.

The Token Efficiency Stack

• Route reasoning heavy tasks like data modeling to frontier models.
• Route simple tasks like writing column descriptions to cheaper models.
• Bring your own LLM, including open source, to control costs and meet governance requirements.
• Avoid brute forcing one model into every specialized task.

The Four High Value Data Use Cases

• ELT pipeline development and debugging.
• Data infrastructure optimization, with cost reductions of 30 to 40 percent.
• Governance reporting and sensitive data tracking.
• Legacy stack migrations without paying a services firm millions.

https://www.altimate.ai/

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

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

https://www.hssv.org

⁠⁠https://aiforfounders.co⁠⁠

https://www.youtube.com/@AIforfounders1